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💋 Affectionate displays boost relationship satisfaction across cultures despite varying social norms
https://phys.org/news/2025-07-affectionate-displays-boost-relationship-satisfaction.html

Affectionate displays boost relationship satisfaction across cultures despite varying social norms
Displays of affection, such as hugging, kissing and holding hands, can be good for your health. They can reduce stress, boost mood and improve quality of life. Being affectionate in both public and private is also linked to happier relationships. But how much do differences in how willing partners are to show affection influence relationship satisfaction?

Setting aside copyright/commercial/other aspects, a thought about writing blog posts that LLMs train on. When people write posts, they (a) feel good about helping others, and (b) hope to get some credit and visibility for doing so.
When mediated through LLMs, no longer the satisfaction that your consumer is a human (who might comment, thank, share, etc.); nor the cred that comes from people remembering the author, posting on HN, etc.
That is, it totally destroys the incentive str…

Replaced article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- It is Giving Major Satisfaction: Why Fairness Matters for Software Practitioners
Emeralda Sesari, Federica Sarro, Ayushi Rastogi

Exploring the Potential of LLMs for Serendipity Evaluation in Recommender Systems
Li Kang, Yuhan Zhao, Li Chen
https://arxiv.org/abs/2507.17290 https://arx…

Exploring the Potential of LLMs for Serendipity Evaluation in Recommender Systems
Serendipity plays a pivotal role in enhancing user satisfaction within recommender systems, yet its evaluation poses significant challenges due to its inherently subjective nature and conceptual ambiguity. Current algorithmic approaches predominantly rely on proxy metrics for indirect assessment, often failing to align with real user perceptions, thus creating a gap. With large language models (LLMs) increasingly revolutionizing evaluation methodologies across various human annotation tasks, we…

Between Filters and Feeds: Investigating Douyin and WeChat's Influence on Chinese Adolescent Body Image
Jianfeng Lan, Yingjia Huang
https://arxiv.org/abs/2507.17755 https://…

Between Filters and Feeds: Investigating Douyin and WeChat's Influence on Chinese Adolescent Body Image
In the digital era, social media platforms play a pivotal role in shaping adolescents' body image perceptions. This study examines how Douyin and WeChat, two contrasting Chinese social media platforms, influence body image among Chinese male adolescents. Employing a platformization perspective, we surveyed 395 male adolescents aged 10 to 24 using the Multidimensional Body-Self Relations Questionnaire-Appearance Scales (MBSRQ-AS) to assess self-evaluation and body satisfaction. Our findings reve…

Going Beyond Twin-width? CSPs with Unbounded Domain and Few Variables
Peter Jonsson, Victor Lagerkvist, Jorke M. de Vlas, Magnus Wahlstr\"om
https://arxiv.org/abs/2508.16389

Going Beyond Twin-width? CSPs with Unbounded Domain and Few Variables
We study a model of constraint satisfaction problems geared towards instances with few variables but with domain of unbounded size (udCSP). Our model is inspired by recent work on FPT algorithms for MinCSP where frequently both upper and lower bounds on the parameterized complexity of a problem correspond to $k$-variable udCSPs; e.g., the FPT algorithms for Boolean MinCSP (Kim et al., SODA 2023) and Directed Multicut with three cut requests (Hatzel et al., SODA 2023) both reduce to k-variable u…

Weak Supervision Techniques towards Enhanced ASR Models in Industry-level CRM Systems
Zhongsheng Wang, Sijie Wang, Jia Wang, Yung-I Liang, Yuxi Zhang, Jiamou Liu
https://arxiv.org/abs/2507.16843

Weak Supervision Techniques towards Enhanced ASR Models in Industry-level CRM Systems
In the design of customer relationship management (CRM) systems, accurately identifying customer types and offering personalized services are key to enhancing customer satisfaction and loyalty. However, this process faces the challenge of discerning customer voices and intentions, and general pre-trained automatic speech recognition (ASR) models make it difficult to effectively address industry-specific speech recognition tasks. To address this issue, we innovatively proposed a solution for fin…

Constraint satisfaction problems, compactness and non-measurable sets
Claude Tardif
https://arxiv.org/abs/2508.14838 https://arxiv.org/pdf/2508.14838

Constraint satisfaction problems, compactness and non-measurable sets
A finite relational structure A is called compact if for any infinite relational structure B of the same type, the existence of a homomorphism from B to A is equivalent to the existence of homomorphisms from all finite substructures of B to A. We show that if A has width one, then the compactness of A can be proved in the axiom system of Zermelo and Fraenkel, but otherwise, the compactness of A implies the existence of non-measurable sets in 3-space.

A Unifying Framework for Semiring-Based Constraint Logic Programming With Negation (full version)
Jeroen Spaans, Jesse Heyninck
https://arxiv.org/abs/2507.16067

A Unifying Framework for Semiring-Based Constraint Logic Programming With Negation (full version)
Constraint Logic Programming (CLP) is a logic programming formalism used to solve problems requiring the consideration of constraints, like resource allocation and automated planning and scheduling. It has previously been extended in various directions, for example to support fuzzy constraint satisfaction, uncertainty, or negation, with different notions of semiring being used as a unifying abstraction for these generalizations. None of these extensions have studied clauses with negation allowe…

Comment on "Storage properties of a quantum perceptron"
Mauro Pastore
https://arxiv.org/abs/2506.18431 https://arxiv.org/pd…

Comment on "Storage properties of a quantum perceptron"
The recent paper "Storage properties of a quantum perceptron" [Phys. Rev. E 110, 024127] considers a quadratic constraint satisfaction problem, motivated by a quantum version of the perceptron. In particular, it derives its critical capacity, the density of constraints at which there is a satisfiability transition. The same problem was considered before in another context (classification of geometrically structured inputs, see [Phys. Rev. Lett. 125, 120601; Phys. Rev. E 102, 032119; J. Stat. Me…

You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control
Giovanni De Toni, Erasmo Purificato, Emilia G\'omez, Bruno Lepri, Andrea Passerini, Cristian Consonni
https://arxiv.org/abs/2507.16829

You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control
Recommenders are significantly shaping online information consumption. While effective at personalizing content, these systems increasingly face criticism for propagating irrelevant, unwanted, and even harmful recommendations. Such content degrades user satisfaction and contributes to significant societal issues, including misinformation, radicalization, and erosion of user trust. Although platforms offer mechanisms to mitigate exposure to undesired content, these mechanisms are often insuffici…

The Constraint Satisfaction Problem Over Multisorted Cores
Dejan Delic, John Marcoux
https://arxiv.org/abs/2508.11540 https://arxiv.org/pdf/2508.11540

The Constraint Satisfaction Problem Over Multisorted Cores
Constraint Satisfaction Problems (CSPs, for short) make up a class of problems with applications in many areas of computer science. The first classification of these problems was given by Schaeffer who showed that every CSP over the domain {0,1} is either in P or is NP-complete. More recently this was shown to hold for all CSPs over finite relational structures independently by Bulatov and Zhuk. Furthermore, they characterized the complexity based solely on the polymorphism algebra of the assoc…

Putin discusses Ukraine war with Erdogan before potential meeting with Zelensky: https://benborges.xyz/2025/08/20/putin-discusses-ukraine-war-with.html

SLM4Offer: Personalized Marketing Offer Generation Using Contrastive Learning Based Fine-Tuning
Vedasamhitha Challapalli, Konduru Venkat Sai, Piyush Pratap Singh, Rupesh Prasad, Arvind Maurya, Atul Singh
https://arxiv.org/abs/2508.15471

SLM4Offer: Personalized Marketing Offer Generation Using Contrastive Learning Based Fine-Tuning
Personalized marketing has emerged as a pivotal strategy for enhancing customer engagement and driving business growth. Academic and industry efforts have predominantly focused on recommendation systems and personalized advertisements. Nonetheless, this facet of personalization holds significant potential for increasing conversion rates and improving customer satisfaction. Prior studies suggest that well-executed personalization strategies can boost revenue by up to 40 percent, underscoring the…

Drafting the Landscape of Computational Musicology Tools: a Survey-Based Approach
Jorge Junior Morgado Vega, Sachin Sharma, Federico Simonetta
https://arxiv.org/abs/2507.15590

Drafting the Landscape of Computational Musicology Tools: a Survey-Based Approach
Since the 60s, musicology has been increasingly impacted by computational tools in various ways, from systematic analysis approaches to modeling of creativity. This article presents a comprehensive assessment of the current state of Computational Musicology tools based on survey data collected from practitioners in the field. We gathered information on tool usage patterns, common analytical tasks, user satisfaction levels, data characteristics, and prioritized features across four distinct doma…

Recursive Gaussian Process Regression with Integrated Monotonicity Assumptions for Control Applications
Ricus Husmann, Sven Weishaupt, Harald Aschemann
https://arxiv.org/abs/2508.14715

Recursive Gaussian Process Regression with Integrated Monotonicity Assumptions for Control Applications
In this paper, we present an extension to the recursive Gaussian Process (RGP) regression that enables the satisfaction of inequality constraints and is well suited for a real-time execution in control applications. The soft inequality constraints are integrated by introducing an additional extended Kalman Filter (EKF) update step using pseudo-measurements. The sequential formulation of the algorithm and several developed heuristics ensure both the performance and a low computational effort of …

Sample-Constrained Black Box Optimization for Audio Personalization
Rajalaxmi Rajagopalan, Yu-Lin Wei, Romit Roy Choudhury
https://arxiv.org/abs/2507.12773

Sample-Constrained Black Box Optimization for Audio Personalization
We consider the problem of personalizing audio to maximize user experience. Briefly, we aim to find a filter $h^*$, which applied to any music or speech, will maximize the user's satisfaction. This is a black-box optimization problem since the user's satisfaction function is unknown. Substantive work has been done on this topic where the key idea is to play audio samples to the user, each shaped by a different filter $h_i$, and query the user for their satisfaction scores $f(h_i)$. A family of …

Study shows the use of emojis in text messaging improves perceived responsiveness and thereby enhances closeness and relationship satisfaction https://phys.org/news/2025-07-emoji-impact-relationship-outcomes-reveals.html

Emoji use may impact relationship outcomes, study reveals
The use of emojis in text messaging improves perceived responsiveness and thereby enhances closeness and relationship satisfaction, according to a study published in the open-access journal PLOS One by Eun Huh from the University of Texas at Austin, U.S.

Can Large Language Models Trigger a Paradigm Shift in Travel Behavior Modeling? Experiences with Modeling Travel Satisfaction
Pengfei Xu, Donggen Wang
https://arxiv.org/abs/2505.23262

Can Large Language Models Trigger a Paradigm Shift in Travel Behavior Modeling? Experiences with Modeling Travel Satisfaction
As a specific domain of subjective well-being, travel satisfaction has attracted much research attention recently. Previous studies primarily use statistical models and, more recently, machine learning models to explore the determinants of travel satisfaction. Both approaches require data from sufficient sample sizes and correct prior statistical assumptions. The emergence of Large Language Models (LLMs) offers a new modeling approach that can overcome the shortcomings of the existing methods. …

Source: Microsoft's Chief Commercial Officer said during a presentation that AI saved Microsoft $500M last year in its call centers and increased satisfaction (Brody Ford/Bloomberg)
https://www.bloomberg.com/news/articles/20

Towards Better Requirements from the Crowd: Developer Engagement with Feature Requests in Open Source Software
Pragyan K C, Rambod Ghandiparsi, Thomas Herron, John Heaps, Mitra Bokaei Hosseini
https://arxiv.org/abs/2507.13553

Towards Better Requirements from the Crowd: Developer Engagement with Feature Requests in Open Source Software
As user demands evolve, effectively incorporating feature requests is crucial for maintaining software relevance and user satisfaction. Feature requests, typically expressed in natural language, often suffer from ambiguity or incomplete information due to communication gaps or the requester's limited technical expertise. These issues can lead to misinterpretation, faulty implementation, and reduced software quality. While seeking clarification from requesters is a common strategy to mitigate th…

I’ve realized I actually enjoy working. What really frustrates me is that the big decisions about my day are made by people who have never even been on my shop floor. On top of that, the things I help make don’t even go to my own community, which just makes it feel even more disconnected.
Honestly, I’d get a lot more satisfaction from my job if we could run things democratically and see our work benefit the people around us. There’s a real sense of pride in building something for your …

On the Constant-Factor Approximability of Minimum Cost Constraint Satisfaction Problems
Ian DeHaan, Neng Huang, Euiwoong Lee
https://arxiv.org/abs/2507.08693

On the Constant-Factor Approximability of Minimum Cost Constraint Satisfaction Problems
We study minimum cost constraint satisfaction problems (MinCostCSP) through the algebraic lens. We show that for any constraint language $Γ$ which has the dual discriminator operation as a polymorphism, there exists a $|D|$-approximation algorithm for MinCostCSP$(Γ)$ where $D$ is the domain. Complementing our algorithmic result, we show that any constraint language $Γ$ where MinCostCSP$(Γ)$ admits a constant-factor approximation must have a \emph{near-unanimity} (NU) polymorphism …

ReviewGraph: A Knowledge Graph Embedding Based Framework for Review Rating Prediction with Sentiment Features
A. J. W. de Vink, Natalia Amat-Lefort, Lifeng Han
https://arxiv.org/abs/2508.13953

ReviewGraph: A Knowledge Graph Embedding Based Framework for Review Rating Prediction with Sentiment Features
In the hospitality industry, understanding the factors that drive customer review ratings is critical for improving guest satisfaction and business performance. This work proposes ReviewGraph for Review Rating Prediction (RRP), a novel framework that transforms textual customer reviews into knowledge graphs by extracting (subject, predicate, object) triples and associating sentiment scores. Using graph embeddings (Node2Vec) and sentiment features, the framework predicts review rating scores thr…

The Richness of CSP Non-redundancy
Joshua Brakensiek, Venkatesan Guruswami, Bart M. P. Jansen, Victor Lagerkvist, Magnus Wahlstr\"om
https://arxiv.org/abs/2507.07942

The Richness of CSP Non-redundancy
In the field of constraint satisfaction problems (CSP), a clause is called redundant if its satisfaction is implied by satisfying all other clauses. An instance of CSP$(P)$ is called non-redundant if it does not contain any redundant clause. The non-redundancy (NRD) of a predicate $P$ is the maximum number of clauses in a non-redundant instance of CSP$(P)$, as a function of the number of variables $n$. Recent progress has shown that non-redundancy is crucially linked to many other important que…

Do Ethical AI Principles Matter to Users? A Large-Scale Analysis of User Sentiment and Satisfaction
Stefan Pasch, Min Chul Cha
https://arxiv.org/abs/2508.05913 https://

Do Ethical AI Principles Matter to Users? A Large-Scale Analysis of User Sentiment and Satisfaction
As AI systems become increasingly embedded in organizational workflows and consumer applications, ethical principles such as fairness, transparency, and robustness have been widely endorsed in policy and industry guidelines. However, there is still scarce empirical evidence on whether these principles are recognized, valued, or impactful from the perspective of users. This study investigates the link between ethical AI and user satisfaction by analyzing over 100,000 user reviews of AI products …

Physics-Informed Neural Networks with Hard Nonlinear Equality and Inequality Constraints
Ashfaq Iftakher, Rahul Golder, M. M. Faruque Hasan
https://arxiv.org/abs/2507.08124 https://arxiv.org/pdf/2507.08124 https://arxiv.org/html/2507.08124
arXiv:2507.08124v1 Announce Type: new
Abstract: Traditional physics-informed neural networks (PINNs) do not guarantee strict constraint satisfaction. This is problematic in engineering systems where minor violations of governing laws can significantly degrade the reliability and consistency of model predictions. In this work, we develop KKT-Hardnet, a PINN architecture that enforces both linear and nonlinear equality and inequality constraints up to machine precision. It leverages a projection onto the feasible region through solving Karush-Kuhn-Tucker (KKT) conditions of a distance minimization problem. Furthermore, we reformulate the nonlinear KKT conditions using log-exponential transformation to construct a general sparse system with only linear and exponential terms, thereby making the projection differentiable. We apply KKT-Hardnet on both test problems and a real-world chemical process simulation. Compared to multilayer perceptrons and PINNs, KKT-Hardnet achieves higher accuracy and strict constraint satisfaction. This approach allows the integration of domain knowledge into machine learning towards reliable hybrid modeling of complex systems.
toXiv_bot_toot

The Influence of Tourist Experience on Revisit Decisions with the Mediation of Tourist Satisfaction
Marsuni H. Muhammad, Ida Hidayanti, Sulfi Abdul Haji, Rahmat Sabuhari
https://arxiv.org/abs/2506.06552

The Influence of Tourist Experience on Revisit Decisions with the Mediation of Tourist Satisfaction
Nusliko Park Ecotourism Area is a combination of natural and artificial tourism with the background of Lake Nusliko, which can be developed as a leading tourist attraction. Marketing strategies can be formulated by analyzing the factors influencing the decision to revisit. The decision to revisit is a key indicator that shows the success of a tourism marketing strategy. This study aimed to test and analyze the relationship between tourist experience and tourist satisfaction with the decision to…

"The disconnection between designers and users has let user interfaces to drift off to a space where only designers are satisfied by them. It is time to remember the users trying to find those buttons, trying to bend those spoons, and to take their satisfaction into account, too."
https://deprogrammat…

Spatial Optimization of Autonomous Vehicle Assignment Based on Distance-Driven Demand and Customer Patience
Niloufar Mirzavand Boroujeni, Nasim Mirzavand Boroujeni, Nima Moradi, Saeed Jamalzadeh
https://arxiv.org/abs/2506.12671

Spatial Optimization of Autonomous Vehicle Assignment Based on Distance-Driven Demand and Customer Patience
Autonomous vehicles (AVs) can improve efficiency, reduce costs, and enhance road safety. They optimize traffic flow, minimize congestion, and support sustainability through shared mobility and reduced fuel consumption. A key challenge in AV deployment is allocating vehicles to parking lots across regions to meet fluctuating demand. Proper allocation reduces delays, lowers costs, and boosts user satisfaction by ensuring timely vehicle availability. This paper explores the impact of customer wait…

A Reproducibility Study of Product-side Fairness in Bundle Recommendation
Huy-Son Nguyen, Yuanna Liu, Masoud Mansoury, Mohammad Alian Nejadi, Alan Hanjalic, Maarten de Rijke
https://arxiv.org/abs/2507.14352

A Reproducibility Study of Product-side Fairness in Bundle Recommendation
Recommender systems are known to exhibit fairness issues, particularly on the product side, where products and their associated suppliers receive unequal exposure in recommended results. While this problem has been widely studied in traditional recommendation settings, its implications for bundle recommendation (BR) remain largely unexplored. This emerging task introduces additional complexity: recommendations are generated at the bundle level, yet user satisfaction and product (or supplier) ex…

Crosslisted article(s) found for cs.LO. https://arxiv.org/list/cs.LO/new
[1/1]:
- The Constraint Satisfaction Problem Over Multisorted Cores
Dejan Delic, John Marcoux

Perception of Brain-Computer Interface Implantation Surgery for Motor, Sensory, and Autonomic Restoration in Spinal Cord Injury and Stroke
Derrick Lin, Tracie Tran, Shravan Thaploo, Jose Gabrielle E. Matias, Joy E. Pixley, Zoran Nenadic, An H. Do
https://arxiv.org/abs/2507.11572

Perception of Brain-Computer Interface Implantation Surgery for Motor, Sensory, and Autonomic Restoration in Spinal Cord Injury and Stroke
(Abridged) Stroke and SCI are conditions that can significantly impact the QoL of survivors in both the physical and psychosocial domains. Both diseases often result in significant motor and sensory impairments that are not fully reversible despite current available therapies. Invasive BCIs have emerged as a promising means to bypass the site of injury and potentially restore motor and sensory function. However, to maximize the utility and participant satisfaction with such technology, particip…

How many federal employees are not satisfied? Using response times to estimate population proportions under the survey variable cause model
Jonathan Auerbach
https://arxiv.org/abs/2506.14915

How many federal employees are not satisfied? Using response times to estimate population proportions under the survey variable cause model
We propose a statistical model to estimate population proportions under the survey variable cause model (Groves 2006), the setting in which the characteristic measured by the survey has a direct causal effect on survey participation. For example, we estimate employee satisfaction from a survey in which the decision of an employee to participate depends on their satisfaction. We model the time at which a respondent 'arrives' to take the survey, leveraging results from the counting processes lite…

Nerd talk.The satisfaction of having your smb shares mounted via the cli on Gentoo surpasses many things.
#gentoo

Sycophancy under Pressure: Evaluating and Mitigating Sycophantic Bias via Adversarial Dialogues in Scientific QA
Kaiwei Zhang, Qi Jia, Zijian Chen, Wei Sun, Xiangyang Zhu, Chunyi Li, Dandan Zhu, Guangtao Zhai
https://arxiv.org/abs/2508.13743

Sycophancy under Pressure: Evaluating and Mitigating Sycophantic Bias via Adversarial Dialogues in Scientific QA
Large language models (LLMs), while increasingly used in domains requiring factual rigor, often display a troubling behavior: sycophancy, the tendency to align with user beliefs regardless of correctness. This tendency is reinforced by preference-based alignment techniques that optimize for user satisfaction but can undermine truthfulness. While relatively benign in casual dialogue, sycophancy poses serious risks in high-stakes settings such as scientific question answering (QA), where model ou…

Safe Deep Reinforcement Learning for Resource Allocation with Peak Age of Information Violation Guarantees
Berire Gunes Reyhan, Sinem Coleri
https://arxiv.org/abs/2507.08653

Safe Deep Reinforcement Learning for Resource Allocation with Peak Age of Information Violation Guarantees
In Wireless Networked Control Systems (WNCSs), control and communication systems must be co-designed due to their strong interdependence. This paper presents a novel optimization theory-based safe deep reinforcement learning (DRL) framework for ultra-reliable WNCSs, ensuring constraint satisfaction while optimizing performance, for the first time in the literature. The approach minimizes power consumption under key constraints, including Peak Age of Information (PAoI) violation probability, tra…

Late for this year, but I just heard about Heavenly Hounds Relaxation Squares. A friend tried them on their dogs and reported great satisfaction. #pets #fireworks
https://

Linear Planar 3-SAT and Its Applications in Planning
Victorien Desbois, Ocan Sankur, Fran\c{c}ois Schwarzentruber
https://arxiv.org/abs/2506.14713 https://…

Linear Planar 3-SAT and Its Applications in Planning
Several fragments of the satisfiability problem have been studied in the literature. Among these, Linear 3-SAT is a satisfaction problem in which each clause (viewed as a set of literals) intersects with at most one other clause; moreover, any pair of clauses have at most one literal in common. Planar 3-SAT is a fragment which requires that the so-called variable-clause graph is planar. Both fragments are NP-complete and have applications in encoding NP-hard planning problems. In this paper, we…

The Hidden Cost of AI Coding #Ai,

Feature Request Analysis and Processing: Tasks, Techniques, and Trends
Feifei Niu, Chuanyi Li, Haosheng Zuo, Jionghan Wu, Xin Xia
https://arxiv.org/abs/2508.12436 https://

Feature Request Analysis and Processing: Tasks, Techniques, and Trends
Feature requests are proposed by users to request new features or enhancements of existing features of software products, which represent users' wishes and demands. Satisfying users' demands can benefit the product from both competitiveness and user satisfaction. Feature requests have seen a rise in interest in the past few years and the amount of research has been growing. However, the diversity in the research topics suggests the need for their collective analysis to identify the challenges a…

Benefiting from Negative yet Informative Feedback by Contrasting Opposing Sequential Patterns
Veronika Ivanova, Evgeny Frolov, Alexey Vasilev
https://arxiv.org/abs/2508.14786 ht…

Benefiting from Negative yet Informative Feedback by Contrasting Opposing Sequential Patterns
We consider the task of learning from both positive and negative feedback in a sequential recommendation scenario, as both types of feedback are often present in user interactions. Meanwhile, conventional sequential learning models usually focus on considering and predicting positive interactions, ignoring that reducing items with negative feedback in recommendations improves user satisfaction with the service. Moreover, the negative feedback can potentially provide a useful signal for more acc…

Talking Surveys: How Photorealistic Embodied Conversational Agents Shape Response Quality, Engagement, and Satisfaction
Matus Krajcovic, Peter Demcak, Eduard Kuric
https://arxiv.org/abs/2508.02376

Talking Surveys: How Photorealistic Embodied Conversational Agents Shape Response Quality, Engagement, and Satisfaction
Embodied conversational agents (ECAs) are increasingly more realistic and capable of dynamic conversations. In online surveys, anthropomorphic agents could help address issues like careless responding and satisficing, which originate from the lack of personal engagement and perceived accountability. However, there is a lack of understanding of how ECAs in user experience research may affect participant engagement, satisfaction, and the quality of responses. As a proof of concept, we propose an …

Proof complexity of Mal'tsev CSP
Azza Gaysin
https://arxiv.org/abs/2508.00396 https://arxiv.org/pdf/2508.00396

Proof complexity of Mal'tsev CSP
Constraint Satisfaction Problems (CSPs) form a broad class of combinatorial problems, which can be formulated as homomorphism problems between relational structures. The CSP dichotomy theorem classifies all such problems over finite domains into two categories: NP-complete and polynomial-time, see Zhuk (2017), Bulatov (2017). Polynomial-time CSPs can be further subdivided into smaller subclasses. Mal'tsev CSPs are defined by the property that every relation in the problem is invariant under a M…

This https://arxiv.org/abs/2308.13140 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Learn With Imagination: Safe Set Guided State-wise Constrained Policy Optimization
Deep reinforcement learning (RL) excels in various control tasks, yet the absence of safety guarantees hampers its real-world applicability. In particular, explorations during learning usually results in safety violations, while the RL agent learns from those mistakes. On the other hand, safe control techniques ensure persistent safety satisfaction but demand strong priors on system dynamics, which is usually hard to obtain in practice. To address these problems, we present Safe Set Guided Stat…

Greed is slow on sparse graphs of oriented valued constraints
Artem Kaznatcheev, Sofia Vazquez Alferez
https://arxiv.org/abs/2506.11662 https://

Greed is slow on sparse graphs of oriented valued constraints
Greedy local search is especially popular for solving valued constraint satisfaction problems (VCSPs). Since any method will be slow for some VCSPs, we ask: what is the simplest VCSP on which greedy local search is slow? We construct a VCSP on 6n Boolean variables for which greedy local search takes 7(2^n - 1) steps to find the unique peak. Our VCSP is simple in two ways. First, it is very sparse: its constraint graph has pathwidth 2 and maximum degree 3. This is the simplest VCSP on which some…

Measuring AI Alignment with Human Flourishing
Elizabeth Hilliard, Akshaya Jagadeesh, Alex Cook, Steele Billings, Nicholas Skytland, Alicia Llewellyn, Jackson Paull, Nathan Paull, Nolan Kurylo, Keatra Nesbitt, Robert Gruenewald, Anthony Jantzi, Omar Chavez
https://arxiv.org/abs/2507.07787…

Measuring AI Alignment with Human Flourishing
This paper introduces the Flourishing AI Benchmark (FAI Benchmark), a novel evaluation framework that assesses AI alignment with human flourishing across seven dimensions: Character and Virtue, Close Social Relationships, Happiness and Life Satisfaction, Meaning and Purpose, Mental and Physical Health, Financial and Material Stability, and Faith and Spirituality. Unlike traditional benchmarks that focus on technical capabilities or harm prevention, the FAI Benchmark measures AI performance on h…

How Warm-Glow Alters the Usability of Technology
Antonios Saravanos (New York University)
https://arxiv.org/abs/2506.14720 https://ar…

How Warm-Glow Alters the Usability of Technology
As technology increasingly aligns with users' personal values, traditional models of usability, focused on functionality and specifically effectiveness, efficiency, and satisfaction, may not fully capture how people perceive and evaluate it. This study investigates how the warm-glow phenomenon, the positive feeling associated with doing good, shapes perceived usability. An experimental approach was taken in which participants evaluated a hypothetical technology under conditions designed to evok…

This https://arxiv.org/abs/2506.01222 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Learning collective variables that preserve transition rates
Collective variables (CVs) play a crucial role in capturing rare events in high-dimensional systems, motivating the continual search for principled approaches to their design. In this work, we revisit the framework of quantitative coarse graining and identify the orthogonality condition from Legoll and Lelievre (2010) as a key criterion for constructing CVs that accurately preserve the statistical properties of the original process. We establish that satisfaction of the orthogonality condition …

A Fuzzy Approach to Project Success: Measuring What Matters
Jo\~ao Granja-Correia, Remedios Hern\'andez-Linares, Luca Ferranti, Arm\'enio Rego
https://arxiv.org/abs/2507.12653

A Fuzzy Approach to Project Success: Measuring What Matters
This paper introduces a novel approach to project success evaluation by integrating fuzzy logic into an existing construct. Traditional Likert-scale measures often overlook the context-dependent and multifaceted nature of project success. The proposed hierarchical Type-1 Mamdani fuzzy system prioritizes sustained positive impact for end-users, reducing emphasis on secondary outcomes like stakeholder satisfaction and internal project success. This dynamic approach may provide a more accurate mea…

Solving Random Planted CSPs below the $n^{k/2}$ Threshold
Arpon Basu, Jun-Ting Hsieh, Andrew D. Lin, Peter Manohar
https://arxiv.org/abs/2507.10833 https:/…

Solving Random Planted CSPs below the $n^{k/2}$ Threshold
We present a family of algorithms to solve random planted instances of any $k$-ary Boolean constraint satisfaction problem (CSP). A randomly planted instance of a Boolean CSP is generated by (1) choosing an arbitrary planted assignment $x^*$, and then (2) sampling constraints from a particular "planting distribution" designed so that $x^*$ will satisfy every constraint. Given an $n$ variable instance of a $k$-ary Boolean CSP with $m$ constraints, our algorithm runs in time $n^{O(\ell)}$ for a c…

A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions
Dhruv S. Kushwaha, Zoleikha A. Biron
https://arxiv.org/abs/2508.09128 https://…

A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions
Reinforcement learning (RL) has proven to be particularly effective in solving complex decision-making problems for a wide range of applications. From a control theory perspective, RL can be considered as an adaptive optimal control scheme. Lyapunov and barrier functions are the most commonly used certificates to guarantee system stability for a proposed/derived controller and constraint satisfaction guarantees, respectively, in control theoretic approaches. However, compared to theoretical gua…

Learning from Natural Language Feedback for Personalized Question Answering
Alireza Salemi, Hamed Zamani
https://arxiv.org/abs/2508.10695 https://arxiv.org…

Learning from Natural Language Feedback for Personalized Question Answering
Personalization is crucial for enhancing both the effectiveness and user satisfaction of language technologies, particularly in information-seeking tasks like question answering. Current approaches for personalizing large language models (LLMs) often rely on retrieval-augmented generation (RAG), followed by reinforcement learning with scalar reward signals to teach models how to use retrieved personal context. We believe that these scalar rewards sometimes provide weak, non-instructive feedback…

Runtime Verification for LTL in Stochastic Systems
Javier Esparza, Vincent Fischer
https://arxiv.org/abs/2508.07963 https://arxiv.org/pdf/2508.07963…

Runtime Verification for LTL in Stochastic Systems
Runtime verification encompasses several lightweight techniques for checking whether a system's current execution satisfies a given specification. We focus on runtime verification for Linear Temporal Logic (LTL). Previous work describes monitors which produce, at every time step one of three outputs - true, false, or inconclusive - depending on whether the observed execution prefix definitively determines satisfaction of the formula. However, for many LTL formulas, such as liveness properties, …

Replaced article(s) found for cond-mat.dis-nn. https://arxiv.org/list/cond-mat.dis-nn/new
[1/1]:
- Beyond-mean-field fluctuations for the solution of constraint satisfaction problems
Foos, Epping, Grundler, Ciobanu, Singh, Bode, Helias, Dahmen

Do Streetscapes Still Matter for Customer Ratings of Eating and Drinking Establishments in Car-Dependent Cities?
Chaeyeon Han, Seung Jae Lieu, Uijeong Hwang, Subhrajit Guhathakurta
https://arxiv.org/abs/2508.06513

Do Streetscapes Still Matter for Customer Ratings of Eating and Drinking Establishments in Car-Dependent Cities?
This study examines how indoor and outdoor aesthetics, streetscapes, and neighborhood features shape customer satisfaction at eating and dining establishments (EDEs) across different urban contexts, varying in car dependency, in Washington, DC. Using review photos and street view images, computer vision models quantified perceived safety and visual appeal. Ordinal logistic regression analyzed their effects on Yelp ratings. Findings reveal that both indoor and outdoor environments significantly …

An Online A/B Testing Decision Support System for Web Usability Assessment Based on a Linguistic Decision-making Methodology: Case of Study a Virtual Learning Environment
Noe Zerme\~no, Cristina Zuheros, Lucas Daniel Del Rosso Calache, Francisco Herrera, Rosana Montes
https://arxiv.org/abs/2507.12118

An Online A/B Testing Decision Support System for Web Usability Assessment Based on a Linguistic Decision-making Methodology: Case of Study a Virtual Learning Environment
In recent years, attention has increasingly focused on enhancing user satisfaction with user interfaces, spanning both mobile applications and websites. One fundamental aspect of human-machine interaction is the concept of web usability. In order to assess web usability, the A/B testing technique enables the comparison of data between two designs. Expanding the scope of tests to include the designs being evaluated, in conjunction with the involvement of both real and fictional users, presents a…

@… There's a wave of satisfaction engulfing me when I purge 486 fonts and related files. That's bloat for me; it was put there without me selecting it 🤪
The overall Slackware install, with Hyprland, labwc, niri and all applications I could possibly want still remains comfortably below 25 gb, video, music, some flatpaks also included. S…

Distributionally Robust System Level Synthesis With Output Feedback Affine Control Policy
Yun Li, Jicheng Shi, Colin N. Jones, Neil Yorke-Smith, Tamas Keviczky
https://arxiv.org/abs/2508.05466

Distributionally Robust System Level Synthesis With Output Feedback Affine Control Policy
This paper studies the finite-horizon robust optimal control of linear systems subject to model mismatch and additive stochastic disturbances. Utilizing the system level synthesis (SLS) parameterization, we propose a novel SLS design using output-feedback affine control policy and extend it to a distributionally robust setting to improve system resilience by minimizing the cost function while ensuring constraint satisfaction against the worst-case uncertainty distribution. The scopes of model m…

Replaced article(s) found for math.LO. https://arxiv.org/list/math.LO/new
[1/1]:
- Satisfaction is not absolute
Joel David Hamkins (University of Notre Dame), Ruizhi Yang (Fudan University, Shanghai)

This https://arxiv.org/abs/2210.07383 has been replaced.
link: https://scholar.google.com/scholar?q=a

Notes on CSPs and Polymorphisms
These are notes from a multi-year learning seminar on the algebraic approach to Constraint Satisfaction Problems (CSPs). The main topics covered are the theory of algebraic structures with few subpowers, the theory of absorbing subalgebras and its applications to studying CSP templates which can be solved by local consistency methods, and the dichotomy theorem for conservative CSP templates. Subsections and appendices cover supplementary material.

This https://arxiv.org/abs/2402.00719 has been replaced.
link: https://scholar.google.com/scholar?q=a

Geometric Contact Potential
Barrier potentials gained popularity as a means for robust contact handling in physical modeling and for modeling self-avoiding shapes. The key to the success of these approaches is adherence to geometric constraints, i.e., avoiding intersections, which are the cause of most robustness problems in complex deformation simulation with contact. However, existing barrier-potential methods may lead to spurious forces and imperfect satisfaction of the geometric constraints. They may have strong resol…

FPT Parameterisations of Fractional and Generalised Hypertree Width
Matthias Lanzinger, Igor Razgon, Daniel Unterberger
https://arxiv.org/abs/2507.11080 ht…

FPT Parameterisations of Fractional and Generalised Hypertree Width
We present the first fixed-parameter tractable (fpt) algorithms for precisely determining several central hypergraph decomposition parameters, including generalized hypertree width, fractional hypertree width, and adaptive width. Despite the recognized importance of these measures in complexity theory, databases, and constraint satisfaction, no exact fpt algorithms for any of them had previously been known. Our results are obtained for hypergraph classes of bounded rank and bounded degree. Ou…

Classification of Quality Characteristics in Online User Feedback using Linguistic Analysis, Crowdsourcing and LLMs
Eduard C. Groen, Fabiano Dalpiaz, Martijn van Vliet, Boris Winter, Joerg Doerr, Sjaak Brinkkemper
https://arxiv.org/abs/2506.11722

Classification of Quality Characteristics in Online User Feedback using Linguistic Analysis, Crowdsourcing and LLMs
Software qualities such as usability or reliability are among the strongest determinants of mobile app user satisfaction and constitute a significant portion of online user feedback on software products, making it a valuable source of quality-related feedback to guide the development process. The abundance of online user feedback warrants the automated identification of quality characteristics, but the online user feedback's heterogeneity and the lack of appropriate training corpora limit the a…

VIVA: Virtual Healthcare Interactions Using Visual Analytics, With Controllability Through Configuration
J\"urgen Bernard, Mara Solen, Helen Novak Lauscher, Kurtis Stewart, Kendall Ho, Tamara Munzner
https://arxiv.org/abs/2508.09386

VIVA: Virtual Healthcare Interactions Using Visual Analytics, With Controllability Through Configuration
At the beginning of the COVID-19 pandemic, HealthLink BC (HLBC) rapidly integrated physicians into the triage process of their virtual healthcare service to improve patient outcomes and satisfaction with this service and preserve health care system capacity. We present the design and implementation of a visual analytics tool, VIVA (Virtual healthcare Interactions using Visual Analytics), to support HLBC in analysing various forms of usage data from the service. We abstract HLBC's data and data …

This https://arxiv.org/abs/2404.17589 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

An Offline Reinforcement Learning Algorithm Customized for Multi-Task Fusion in Large-Scale Recommender Systems
As the last critical stage of RSs, Multi-Task Fusion (MTF) is responsible for combining multiple scores outputted by Multi-Task Learning (MTL) into a final score to maximize user satisfaction, which determines the ultimate recommendation results. Recently, to optimize long-term user satisfaction within a recommendation session, Reinforcement Learning (RL) is used for MTF in the industry. However, the offline RL algorithms used for MTF so far have the following severe problems: 1) to avoid out-o…

Service Time Window Design in Last-Mile Delivery
Davod Hosseini, Borzou Rostami, Mojtaba Araghi
https://arxiv.org/abs/2508.01032 https://arxiv.org/pdf/2508…

Service Time Window Design in Last-Mile Delivery
Our study focuses on designing reliable service time windows for customers in a last-mile delivery system to boost dependability and enhance customer satisfaction. To construct time windows for a pre-determined route (e.g., provided by commercial routing software), we introduce two criteria that balance window length and the risk of violation. The service provider can allocate different penalties reflecting risk tolerances to each criterion, resulting in various time windows with varying levels…

Learning collective variables that preserve transition rates
Shashank Sule, Arnav Mehta, Maria K. Cameron
https://arxiv.org/abs/2506.01222 https://

Learning collective variables that preserve transition rates
Collective variables (CVs) play a crucial role in capturing rare events in high-dimensional systems, motivating the continual search for principled approaches to their design. In this work, we revisit the framework of quantitative coarse graining and identify the orthogonality condition from Legoll and Lelievre (2010) as a key criterion for constructing CVs that accurately preserve the statistical properties of the original process. We establish that satisfaction of the orthogonality condition …

Z3Guide: A Scalable, Student-Centered, and Extensible Educational Environment for Logic Modeling
Ruanqianqian Huang, Ayana Monroe, Peli de Halleux, Sorin Lerner, Nikolaj Bj{\o}rner
https://arxiv.org/abs/2506.08294

Z3Guide: A Scalable, Student-Centered, and Extensible Educational Environment for Logic Modeling
Constraint-satisfaction problems (CSPs) are ubiquitous, ranging from budgeting for grocery shopping to verifying software behavior. Logic modeling helps solve CSPs programmatically using SMT solvers. Despite its importance in many Computer Science disciplines, resources for teaching and learning logic modeling are scarce and scattered, and challenges remain in designing educational environments for logic modeling that are accessible and meet the needs of teachers and students. This paper explor…

PCPP-Based Reconfiguration Inapproximability: Query Complexity vs. Soundness Gap Trade-offs
Venkatesan Guruswami, Xuandi Ren, Kewen Wu
https://arxiv.org/abs/2507.01192

PCPP-Based Reconfiguration Inapproximability: Query Complexity vs. Soundness Gap Trade-offs
The Reconfiguration Inapproximability Hypothesis (RIH), recently established by Hirahara-Ohsaka (STOC'24) and Karthik-Manurangsi (ECCC'24), studies the hardness of reconfiguring one solution into another in constraint satisfaction problems (CSP) when restricted to approximate intermediate solutions. In this work, we make a tighter connection between RIH's soundness gap and that of probabilistically checkable proofs of proximity (PCPP). Consequently, we achieve an improved trade-off between soun…

Update-Aware Robust Optimal Model Predictive Control for Nonlinear Systems
J. Wehbeh, E. C. Kerrigan
https://arxiv.org/abs/2506.01729 https://

Update-Aware Robust Optimal Model Predictive Control for Nonlinear Systems
Robust optimal or min-max model predictive control (MPC) approaches aim to guarantee constraint satisfaction over a known, bounded uncertainty set while minimizing a worst-case performance bound. Traditionally, these methods compute a trajectory that meets the desired properties over a fixed prediction horizon, apply a portion of the resulting input, and then re-solve the MPC problem using newly obtained measurements at the next time step. However, this approach fails to account for the fact th…

An Expansion-Based Approach for Quantified Integer Programming
Michael Hartisch, Leroy Chew
https://arxiv.org/abs/2506.04452 https://…

An Expansion-Based Approach for Quantified Integer Programming
Quantified Integer Programming (QIP) bridges multiple domains by extending Quantified Boolean Formulas (QBF) to incorporate general integer variables and linear constraints while also generalizing Integer Programming through variable quantification. As a special case of Quantified Constraint Satisfaction Problems (QCSP), QIP provides a versatile framework for addressing complex decision-making scenarios. Additionally, the inclusion of a linear objective function enables QIP to effectively model…

This https://arxiv.org/abs/1512.03127 has been replaced.
link: https://scholar.google.com/scholar?q=a

Flexible constraint satisfiability and a problem in semigroup theory
We examine some flexible notions of constraint satisfaction, observing some relationships between model theoretic notions of universal Horn class membership and robust satisfiability. We show the \texttt{NP}-completeness of $2$-robust monotone 1-in-3 3SAT in order to give very small examples of finite algebras with \texttt{NP}-hard variety membership problem. In particular we give a $3$-element algebra with this property, and solve a widely stated problem by showing that the $6$-element Brandt …

Distributed Constraint-coupled Resource Allocation: Anytime Feasibility and Violation Robustness
Wenwen Wu, Shanying Zhu, Cailian Chen, Xinping Guan
https://arxiv.org/abs/2508.02164

Distributed Constraint-coupled Resource Allocation: Anytime Feasibility and Violation Robustness
This paper considers distributed resource allocation problems (DRAPs) with a coupled constraint for real-time systems. Based on primal-dual methods, we adopt a control perspective for optimization algorithm design by synthesizing a safe feedback controller using control barrier functions to enforce constraint satisfaction. On this basis, a distributed anytime-feasible resource allocation (DanyRA) algorithm is proposed. It is shown that DanyRA algorithm converges to the exact optimal solution of…

This https://arxiv.org/abs/2505.05660 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Not Like Us, Hunty: Measuring Perceptions and Behavioral Effects of Minoritized Anthropomorphic Cues in LLMs
As large language models (LLMs) increasingly adapt and personalize to diverse sets of users, there is an increased risk of systems appropriating sociolects, i.e., language styles or dialects that are associated with specific minoritized lived experiences (e.g., African American English, Queer slang). In this work, we examine whether sociolect usage by an LLM agent affects user reliance on its outputs and user perception (satisfaction, frustration, trust, and social presence). We designed and co…

#Anarchism #Syndicalism #AnarchoSyndicalism #Anarchy

Enhancing Serendipity Recommendation System by Constructing Dynamic User Knowledge Graphs with Large Language Models
Qian Yong, Yanhui Li, Jialiang Shi, Yaguang Dou, Tian Qi
https://arxiv.org/abs/2508.04032

Enhancing Serendipity Recommendation System by Constructing Dynamic User Knowledge Graphs with Large Language Models
The feedback loop in industrial recommendation systems reinforces homogeneous content, creates filter bubble effects, and diminishes user satisfaction. Recently, large language models(LLMs) have demonstrated potential in serendipity recommendation, thanks to their extensive world knowledge and superior reasoning capabilities. However, these models still face challenges in ensuring the rationality of the reasoning process, the usefulness of the reasoning results, and meeting the latency requirem…

AI Should Be More Human, Not More Complex
Carlo Esposito (Eyed Softwares, Aploide Softwares)
https://arxiv.org/abs/2508.04713 https://arxiv.org/pdf/2508.04…

AI Should Be More Human, Not More Complex
Large Language Models (LLMs) in search applications increasingly prioritize verbose, lexically complex responses that paradoxically reduce user satisfaction and engagement. Through a comprehensive study of 10.000 (est.) participants comparing responses from five major AI-powered search systems, we demonstrate that users overwhelmingly prefer concise, source-attributed responses over elaborate explanations. Our analysis reveals that current AI development trends toward "artificial sophistication…

This https://arxiv.org/abs/2505.22124 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A Nurse Staffing and Scheduling Problem with Bounded Flexibility and Demand Uncertainty
Nurse staffing and scheduling are persistent challenges in healthcare due to demand fluctuations and individual nurse preferences. This study introduces the concept of bounded flexibility, balancing nurse satisfaction with strict rostering rules, particularly a real-world time regularity policy from a major hospital in Singapore. We model the problem as a multi-stage stochastic program to address evolving demand, optimizing both aggregate staffing and detailed scheduling decisions. A reformulat…

Evaluating Generative AI Tools for Personalized Offline Recommendations: A Comparative Study
Rafael Salinas-Buestan, Otto Parra, Nelly Condori-Fernandez, Maria Fernanda Granda
https://arxiv.org/abs/2508.03710

Evaluating Generative AI Tools for Personalized Offline Recommendations: A Comparative Study
Background: Generative AI tools have become increasingly relevant in supporting personalized recommendations across various domains. However, their effectiveness in health-related behavioral interventions, especially those aiming to reduce the use of technology, remains underexplored. Aims: This study evaluates the performance and user satisfaction of the five most widely used generative AI tools when recommending non-digital activities tailored to individuals at risk of repetitive strain injur…

Generating Satisfiable Benchmark Instances for Stable Roommates Problems with Optimization
Baturay Y{\i}lmaz, Esra Erdem
https://arxiv.org/abs/2507.20013 https://

Generating Satisfiable Benchmark Instances for Stable Roommates Problems with Optimization
While the existence of a stable matching for the stable roommates problem possibly with incomplete preference lists (SRI) can be decided in polynomial time, SRI problems with some fairness criteria are intractable. Egalitarian SRI that tries to maximize the total satisfaction of agents if a stable matching exists, is such a hard variant of SRI. For experimental evaluations of methods to solve these hard variants of SRI, several well-known algorithms have been used to randomly generate benchmark…

Teaming in the AI Era: AI-Augmented Frameworks for Forming, Simulating, and Optimizing Human Teams
Mohammed Almutairi
https://arxiv.org/abs/2506.05265 http…

Teaming in the AI Era: AI-Augmented Frameworks for Forming, Simulating, and Optimizing Human Teams
Effective teamwork is essential across diverse domains. During the team formation stage, a key challenge is forming teams that effectively balance user preferences with task objectives to enhance overall team satisfaction. In the team performing stage, maintaining cohesion and engagement is critical for sustaining high team performance. However, existing computational tools and algorithms for team optimization often rely on static data inputs, narrow algorithmic objectives, or solutions tailore…

Who's Sorry Now: User Preferences Among Rote, Empathic, and Explanatory Apologies from LLM Chatbots
Zahra Ashktorab, Alessandra Buccella, Jason D'Cruz, Zoe Fowler, Andrew Gill, Kei Yan Leung, P. D. Magnus, John Richards, Kush R. Varshney
https://arxiv.org/abs/2507.02745

Who's Sorry Now: User Preferences Among Rote, Empathic, and Explanatory Apologies from LLM Chatbots
As chatbots driven by large language models (LLMs) are increasingly deployed in everyday contexts, their ability to recover from errors through effective apologies is critical to maintaining user trust and satisfaction. In a preregistered study with Prolific workers (N=162), we examine user preferences for three types of apologies (rote, explanatory, and empathic) issued in response to three categories of common LLM mistakes (bias, unfounded fabrication, and factual errors). We designed a pairw…

Sustainability Evaluation Metrics for Recommender Systems
Alexander Felfernig, Damian Garber, Viet-Man Le, Sebastian Lubos, Thi Ngoc Trang Tran
https://arxiv.org/abs/2507.22520 …

Sustainability Evaluation Metrics for Recommender Systems
Sustainability-oriented evaluation metrics can help to assess the quality of recommender systems beyond wide-spread metrics such as accuracy, precision, recall, and satisfaction. Following the United Nations`s sustainable development goals (SDGs), such metrics can help to analyse the impact of recommender systems on environmental, social, and economic aspects. We discuss different basic sustainability evaluation metrics for recommender systems and analyze their applications.

The SPACE of AI: Real-World Lessons on AI's Impact on Developers
Brian Houck, Travis Lowdermilk, Cody Beyer, Steven Clarke, Ben Hanrahan
https://arxiv.org/abs/2508.00178 htt…

The SPACE of AI: Real-World Lessons on AI's Impact on Developers
As artificial intelligence (AI) tools become increasingly embedded in software development workflows, questions persist about their true impact on developer productivity and experience. This paper presents findings from a mixed-methods study examining how developers perceive AI's influence across the dimensions of the SPACE framework: Satisfaction, Performance, Activity, Collaboration and Efficiency. Drawing on survey responses from over 500 developers and qualitative insights from interviews a…

A Theory-driven and AI-enhanced Simulation Platform for Cultivating Nutrition Literacy
Shan Li, Guozhu Ding
https://arxiv.org/abs/2507.02138 https://

A Theory-driven and AI-enhanced Simulation Platform for Cultivating Nutrition Literacy
This study introduces and evaluates Healthy Choice, an innovative theory-driven and AI-enhanced simulation platform designed to cultivate nutrition literacy through interactive scenario-based learning experiences. We collected feedback from 114 university students with diverse backgrounds who completed simulated product selection scenarios. Quantitative ratings of usefulness and ease of use demonstrated high user satisfaction.

Proposing a Semantic Movie Recommendation System Enhanced by ChatGPT's NLP Results
Ali Fallahi, Azam Bastanfard, Amineh Amini, Hadi Saboohi
https://arxiv.org/abs/2507.21770 …

Proposing a Semantic Movie Recommendation System Enhanced by ChatGPT's NLP Results
The importance of recommender systems on the web has grown, especially in the movie industry, with a vast selection of options to watch. To assist users in traversing available items and finding relevant results, recommender systems analyze operational data and investigate users' tastes and habits. Providing highly individualized suggestions can boost user engagement and satisfaction, which is one of the fundamental goals of the movie industry, significantly in online platforms. According to re…

Real-time and personalized product recommendations for large e-commerce platforms
Matteo Tolloso, Davide Bacciu, Shahab Mokarizadeh, Marco Varesi
https://arxiv.org/abs/2506.21368 …

Real-time and personalized product recommendations for large e-commerce platforms
We present a methodology to provide real-time and personalized product recommendations for large e-commerce platforms, specifically focusing on fashion retail. Our approach aims to achieve accurate and scalable recommendations with minimal response times, ensuring user satisfaction, leveraging Graph Neural Networks and parsimonious learning methodologies. Extensive experimentation with datasets from one of the largest e-commerce platforms demonstrates the effectiveness of our approach in foreca…

Eye-tracking-Driven Shared Control for Robotic Arms:Wizard of Oz Studies to Assess Design Choices
Anke Fischer-Janzen, Thomas M. Wendt, Daniel G\"orlich, Kristof Van Laerhoven
https://arxiv.org/abs/2505.23147

Eye-tracking-Driven Shared Control for Robotic Arms:Wizard of Oz Studies to Assess Design Choices
Advances in eye-tracking control for assistive robotic arms provide intuitive interaction opportunities for people with physical disabilities. Shared control has gained interest in recent years by improving user satisfaction through partial automation of robot control. We present an eye-tracking-guided shared control design based on insights from state-of-the-art literature. A Wizard of Oz setup was used in which automation was simulated by an experimenter to evaluate the concept without requir…

Metaverse Support Groups for LGBTQ Youth: An Observational Study on Safety, Self-Expression, and Early Intervention
Joe Hasei, Yosuke Matsumoto, Hiroki Kawai, Yuko Okahisa, Manabu Takaki, Toshifumi Ozaki
https://arxiv.org/abs/2507.21079

Metaverse Support Groups for LGBTQ+ Youth: An Observational Study on Safety, Self-Expression, and Early Intervention
This study assessed metaverse-based support groups designed to reduce social isolation and suicide risk among LGBTQ+ youths. Using the Cluster platform, enhanced anonymity, avatar-based self-expression, and accessibility were provided. Key findings showed that 79.2% chose avatars matching their gender identity, reporting high satisfaction (mean: 4.10/5) and low discomfort (mean: 1.79/5). Social confidence significantly improved in virtual spaces compared to real-world interactions (p<0.001), pa…

Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity
Rizal Khoirul Anam
https://arxiv.org/abs/2507.18638 https://

Prompt Engineering and the Effectiveness of Large Language Models in Enhancing Human Productivity
The widespread adoption of large language models (LLMs) such as ChatGPT, Gemini, and DeepSeek has significantly changed how people approach tasks in education, professional work, and creative domains. This paper investigates how the structure and clarity of user prompts impact the effectiveness and productivity of LLM outputs. Using data from 243 survey respondents across various academic and occupational backgrounds, we analyze AI usage habits, prompting strategies, and user satisfaction. The …