Tootfinder

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems
Junli Shao, Jing Dong, Dingzhou Wang, Kowei Shih, Dannier Li, Chengrui Zhou
https://arxiv.org/abs/2506.11421

Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems
With the rapid growth of Internet services, recommendation systems play a central role in delivering personalized content. Faced with massive user requests and complex model architectures, the key challenge for real-time recommendation systems is how to reduce inference latency and increase system throughput without sacrificing recommendation quality. This paper addresses the high computational cost and resource bottlenecks of deep learning models in real-time settings by proposing a combined s…

Lessons Learned from Evaluation of LLM based Multi-agents in Safer Therapy Recommendation
Yicong Wu, Ting Chen, Irit Hochberg, Zhoujian Sun, Ruth Edry, Zhengxing Huang, Mor Peleg
https://arxiv.org/abs/2507.10911

Lessons Learned from Evaluation of LLM based Multi-agents in Safer Therapy Recommendation
Therapy recommendation for chronic patients with multimorbidity is challenging due to risks of treatment conflicts. Existing decision support systems face scalability limitations. Inspired by the way in which general practitioners (GP) manage multimorbidity patients, occasionally convening multidisciplinary team (MDT) collaboration, this study investigated the feasibility and value of using a Large Language Model (LLM)-based multi-agent system (MAS) for safer therapy recommendations. We designe…

BanditWare: A Contextual Bandit-based Framework for Hardware Prediction
Tain\~a Coleman, Hena Ahmed, Ravi Shende, Ismael Perez, \"Ilkay Altinta\c{s}
https://arxiv.org/abs/2506.13730

BanditWare: A Contextual Bandit-based Framework for Hardware Prediction
Distributed computing systems are essential for meeting the demands of modern applications, yet transitioning from single-system to distributed environments presents significant challenges. Misallocating resources in shared systems can lead to resource contention, system instability, degraded performance, priority inversion, inefficient utilization, increased latency, and environmental impact. We present BanditWare, an online recommendation system that dynamically selects the most suitable ha…

lastfm_aminer: Last.fm social graph
This network contains the social graph of last.fm, a site that provides a streaming radio service, where users can search music and get personalized recommendation. A directed edge (i,j) means that user i follows user j.
This network has 136409 nodes and 1685524 edges.
Tags: Social, Online, Unweighted

I started discovering @… last week by writing a #Kotlin recipe that moves Kotlin files according to the official directory structure recommendation. I mentioned some future works, and here they are. In this post, I want to describe how to compute the root packag…

DS4RS: Community-Driven and Explainable Dataset Search Engine for Recommender System Research
Xinyang Shao, Tri Kurniawan Wijaya
https://arxiv.org/abs/2508.10238 https://…

DS4RS: Community-Driven and Explainable Dataset Search Engine for Recommender System Research
Accessing suitable datasets is critical for research and development in recommender systems. However, finding datasets that match specific recommendation task or domains remains a challenge due to scattered sources and inconsistent metadata. To address this gap, we propose a community-driven and explainable dataset search engine tailored for recommender system research. Our system supports semantic search across multiple dataset attributes, such as dataset names, descriptions, and recommendatio…

"Research on the construction and application of problem-method-oriented academic graph empowered by LLM" https://doi.org/10.1007/s10791-025-09675-2
"Nowadays, the volume of literature in each field is huge and is growing rapidly, which posts challenge to researchers’ lit…

DAS: Dual-Aligned Semantic IDs Empowered Industrial Recommender System
Wencai Ye, Mingjie Sun, Shaoyun Shi, Peng Wang, Wenjin Wu, Peng Jiang
https://arxiv.org/abs/2508.10584 htt…

DAS: Dual-Aligned Semantic IDs Empowered Industrial Recommender System
Semantic IDs are discrete identifiers generated by quantizing the Multi-modal Large Language Models (MLLMs) embeddings, enabling efficient multi-modal content integration in recommendation systems. However, their lack of collaborative signals results in a misalignment with downstream discriminative and generative recommendation objectives. Recent studies have introduced various alignment mechanisms to address this problem, but their two-stage framework design still leads to two main limitations…

lastfm_aminer: Last.fm social graph
This network contains the social graph of last.fm, a site that provides a streaming radio service, where users can search music and get personalized recommendation. A directed edge (i,j) means that user i follows user j.
This network has 136409 nodes and 1685524 edges.
Tags: Social, Online, Unweighted

23andMe privacy ombudsman recommends company obtains consent for sale of customer data https://therecord.media/23andme-privacy-ombudsman-recommends-consent-sale

lastfm_aminer: Last.fm social graph
This network contains the social graph of last.fm, a site that provides a streaming radio service, where users can search music and get personalized recommendation. A directed edge (i,j) means that user i follows user j.
This network has 136409 nodes and 1685524 edges.
Tags: Social, Online, Unweighted

Building a Recommendation System Using Amazon Product Co-Purchasing Network
Minghao Liu, Catherine Zhao, Nathan Zhou
https://arxiv.org/abs/2506.02482 https…

Building a Recommendation System Using Amazon Product Co-Purchasing Network
This project develops an online, inductive recommendation system for newly listed products on e-commerce platforms, focusing on suggesting relevant new items to customers as they purchase other products. Using the Amazon Product Co-Purchasing Network Metadata dataset, we construct a co-purchasing graph where nodes represent products and edges capture co-purchasing relationships. To address the challenge of recommending new products with limited information, we apply a modified GraphSAGE method …

My #Series Recommendation 📺
👉 STICK ⛳ 🏌️
if you love Owen Wilson, you will love this funny "feel good" series ❤️

https://www.imdb.com/title/tt31710249/

Six Guidelines for Trustworthy, Ethical and Responsible Automation Design
Matou\v{s} Jel\'inek, Nadine Schlicker, Ewart de Visser
https://arxiv.org/abs/2508.02371 https://…

Six Guidelines for Trustworthy, Ethical and Responsible Automation Design
Calibrated trust in automated systems (Lee and See 2004) is critical for their safe and seamless integration into society. Users should only rely on a system recommendation when it is actually correct and reject it when it is factually wrong. One requirement to achieve this goal is an accurate trustworthiness assessment, ensuring that the user's perception of the system's trustworthiness aligns with its actual trustworthiness, allowing users to make informed decisions about the extent to which …

UK 2025 #Publication
Severe Space Weather Impacts on UK Critical National Infrastructure
A Space weather instrumentation, measurement, modelling and risk (#SWIMMR) Project Report
Priority Recommendation CS6 : "That government and other policymakers ensure that s…

RapidStore: An Efficient Dynamic Graph Storage System for Concurrent Queries
Chiyu Hao, Jixian Su, Shixuan Sun, Hao Zhang, Sen Gao, Jianwen Zhao, Chenyi Zhang, Jieru Zhao, Chen Chen, Minyi Guo
https://arxiv.org/abs/2507.00839

RapidStore: An Efficient Dynamic Graph Storage System for Concurrent Queries
Dynamic graph storage systems are essential for real-time applications such as social networks and recommendation, where graph data continuously evolves. However, they face significant challenges in efficiently handling concurrent read and write operations. We find that existing methods suffer from write queries interfering with read efficiency, substantial time and space overhead due to per-edge versioning, and an inability to balance performance, such as slow searches under concurrent workloa…

lastfm_aminer: Last.fm social graph
This network contains the social graph of last.fm, a site that provides a streaming radio service, where users can search music and get personalized recommendation. A directed edge (i,j) means that user i follows user j.
This network has 136409 nodes and 1685524 edges.
Tags: Social, Online, Unweighted

Lessons from A Large Language Model-based Outdoor Trail Recommendation Chatbot with Retrieval Augmented Generation
Julia Ann Mathew, Suining He
https://arxiv.org/abs/2508.05652 …

Lessons from A Large Language Model-based Outdoor Trail Recommendation Chatbot with Retrieval Augmented Generation
The increasing popularity of outdoor recreational activities (such as hiking and biking) has boosted the demand for a conversational AI system to provide informative and personalized suggestion on outdoor trails. Challenges arise in response to (1) how to provide accurate outdoor trail information via conversational AI; and (2) how to enable usable and efficient recommendation services. To address above, this paper discusses the preliminary and practical lessons learned from developing Judy, an…

Estimation of Treatment Effects Under Nonstationarity via Truncated Difference-in-Q's
Ramesh Johari, Tianyi Peng, Wenqian Xing
https://arxiv.org/abs/2506.05308

Estimation of Treatment Effects Under Nonstationarity via Truncated Difference-in-Q's
Randomized controlled experiments (''A/B testing'') are fundamental for assessing interventions in dynamic technology-driven environments, such as recommendation systems, online marketplaces, and digital health interventions. In these systems, interventions typically impact not only the current state of the system, but also future states; therefore, accurate estimation of the global average treatment effect (or GATE) from experiments requires accounting for the dynamic temporal behavior of the …

lastfm_aminer: Last.fm social graph
This network contains the social graph of last.fm, a site that provides a streaming radio service, where users can search music and get personalized recommendation. A directed edge (i,j) means that user i follows user j.
This network has 136409 nodes and 1685524 edges.
Tags: Social, Online, Unweighted

Towards Efficient and Scalable Distributed Vector Search with RDMA
Xiangyu Zhi, Meng Chen, Xiao Yan, Baotong Lu, Hui Li, Qianxi Zhang, Qi Chen, James Cheng
https://arxiv.org/abs/2507.06653

Towards Efficient and Scalable Distributed Vector Search with RDMA
Similarity-based vector search facilitates many important applications such as search and recommendation but is limited by the memory capacity and bandwidth of a single machine due to large datasets and intensive data read. In this paper, we present CoTra, a system that scales up vector search for distributed execution. We observe a tension between computation and communication efficiency, which is the main challenge for good scalability, i.e., handling the local vectors on each machine indepen…

Dynamic Context-Aware Prompt Recommendation for Domain-Specific AI Applications
Xinye Tang, Haijun Zhai, Chaitanya Belwal, Vineeth Thayanithi, Philip Baumann, Yogesh K Roy
https://arxiv.org/abs/2506.20815

Dynamic Context-Aware Prompt Recommendation for Domain-Specific AI Applications
LLM-powered applications are highly susceptible to the quality of user prompts, and crafting high-quality prompts can often be challenging especially for domain-specific applications. This paper presents a novel dynamic context-aware prompt recommendation system for domain-specific AI applications. Our solution combines contextual query analysis, retrieval-augmented knowledge grounding, hierarchical skill organization, and adaptive skill ranking to generate relevant and actionable prompt sugges…

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…

lastfm_aminer: Last.fm social graph
This network contains the social graph of last.fm, a site that provides a streaming radio service, where users can search music and get personalized recommendation. A directed edge (i,j) means that user i follows user j.
This network has 136409 nodes and 1685524 edges.
Tags: Social, Online, Unweighted

Multi-Modal Multi-Behavior Sequential Recommendation with Conditional Diffusion-Based Feature Denoising
Xiaoxi Cui, Weihai Lu, Yu Tong, Yiheng Li, Zhejun Zhao
https://arxiv.org/abs/2508.05352

Multi-Modal Multi-Behavior Sequential Recommendation with Conditional Diffusion-Based Feature Denoising
The sequential recommendation system utilizes historical user interactions to predict preferences. Effectively integrating diverse user behavior patterns with rich multimodal information of items to enhance the accuracy of sequential recommendations is an emerging and challenging research direction. This paper focuses on the problem of multi-modal multi-behavior sequential recommendation, aiming to address the following challenges: (1) the lack of effective characterization of modal preferences…

Investigating Algorithmic Bias in YouTube Shorts
Mert Can Cakmak, Nitin Agarwal, Diwash Poudel
https://arxiv.org/abs/2507.04605 https://

Investigating Algorithmic Bias in YouTube Shorts
The rapid growth of YouTube Shorts, now serving over 2 billion monthly users, reflects a global shift toward short-form video as a dominant mode of online content consumption. This study investigates algorithmic bias in YouTube Shorts' recommendation system by analyzing how watch-time duration, topic sensitivity, and engagement metrics influence content visibility and drift. We focus on three content domains: the South China Sea dispute, the 2024 Taiwan presidential election, and general YouTub…

An ML-Driven Participant Selection Technique for Federated Recommendation System in Edge-Cloud Computing
Jintao Liu, Mohammad Goudarzi, Adel Nadjaran Toosi
https://arxiv.org/abs/2507.15233

An ML-Driven Participant Selection Technique for Federated Recommendation System in Edge-Cloud Computing
Recommendation systems (RS) personalize content by analyzing user preferences, but typically require centralized collection of user data, raising privacy and scalability concerns. Federated Recommendation Systems (FRS) address these issues by enabling distributed, privacy-preserving model training across edge devices, keeping raw data on-device. Although existing FRS frameworks benefit from on-device feature extraction and privacy preservation, they suffer from heterogeneous device capabilities…

A friend of mine, after watching « Lucifer » on my recommendation, shared this nugget of wisdom: the Lucifer character archetype is basically Ryō Saeba from « City Hunter ». I agree.
#anime #series

A Metric for MLLM Alignment in Large-scale Recommendation
Yubin Zhang, Yanhua Huang, Haiming Xu, Mingliang Qi, Chang Wang, Jiarui Jin, Xiangyuan Ren, Xiaodan Wang, Ruiwen Xu
https://arxiv.org/abs/2508.04963

A Metric for MLLM Alignment in Large-scale Recommendation
Multimodal recommendation has emerged as a critical technique in modern recommender systems, leveraging content representations from advanced multimodal large language models (MLLMs). To ensure these representations are well-adapted, alignment with the recommender system is essential. However, evaluating the alignment of MLLMs for recommendation presents significant challenges due to three key issues: (1) static benchmarks are inaccurate because of the dynamism in real-world applications, (2) e…

lastfm_aminer: Last.fm social graph
This network contains the social graph of last.fm, a site that provides a streaming radio service, where users can search music and get personalized recommendation. A directed edge (i,j) means that user i follows user j.
This network has 136409 nodes and 1685524 edges.
Tags: Social, Online, Unweighted

Breaker: Removing Shortcut Cues with User Clustering for Single-slot Recommendation System
Chao Wang, Yue Zheng, Yujing Zhang, Yan Feng, Zhe Wang, Xiaowei Shi, An You, Yu Chen
https://arxiv.org/abs/2506.00828

Breaker: Removing Shortcut Cues with User Clustering for Single-slot Recommendation System
In a single-slot recommendation system, users are only exposed to one item at a time, and the system cannot collect user feedback on multiple items simultaneously. Therefore, only pointwise modeling solutions can be adopted, focusing solely on modeling the likelihood of clicks or conversions for items by users to learn user-item preferences, without the ability to capture the ranking information among different items directly. However, since user-side information is often much more abundant tha…

Suggest, Complement, Inspire: Story of Two Tower Recommendations at Allegro.com
Aleksandra Osowska-Kurczab, Klaudia Nazarko, Mateusz Marzec, Lidia Wojciechowska, Eli\v{s}ka Kreme\v{n}ov\'a
https://arxiv.org/abs/2508.03702

Suggest, Complement, Inspire: Story of Two Tower Recommendations at Allegro.com
Building large-scale e-commerce recommendation systems requires addressing three key technical challenges: (1) designing a universal recommendation architecture across dozens of placements, (2) decreasing excessive maintenance costs, and (3) managing a highly dynamic product catalogue. This paper presents a unified content-based recommendation system deployed at Allegro.com, the largest e-commerce platform of European origin. The system is built on a prevalent Two Tower retrieval framework, rep…

Audio Prototypical Network For Controllable Music Recommendation
F{\i}rat \"Oncel, Emiliano Penaloza, Haolun Wu, Shubham Gupta, Mirco Ravanelli, Laurent Charlin, Cem Subakan
https://arxiv.org/abs/2508.00194

Audio Prototypical Network For Controllable Music Recommendation
Traditional recommendation systems represent user preferences in dense representations obtained through black-box encoder models. While these models often provide strong recommendation performance, they lack interpretability for users, leaving users unable to understand or control the system's modeling of their preferences. This limitation is especially challenging in music recommendation, where user preferences are highly personal and often evolve based on nuanced qualities like mood, genre, t…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/8]:
- Retrieval and Distill: A Temporal Data Shift-Free Paradigm for Online Recommendation System
Lei Zheng, Ning Li, Weinan Zhang, Yong Yu

lastfm_aminer: Last.fm social graph
This network contains the social graph of last.fm, a site that provides a streaming radio service, where users can search music and get personalized recommendation. A directed edge (i,j) means that user i follows user j.
This network has 136409 nodes and 1685524 edges.
Tags: Social, Online, Unweighted

FIRE: Faithful Interpretable Recommendation Explanations
S. M. F. Sani, Asal Meskin, Mohammad Amanlou, Hamid R. Rabiee
https://arxiv.org/abs/2508.05225 https://

FIRE: Faithful Interpretable Recommendation Explanations
Natural language explanations in recommender systems are often framed as a review generation task, leveraging user reviews as ground-truth supervision. While convenient, this approach conflates a user's opinion with the system's reasoning, leading to explanations that may be fluent but fail to reflect the true logic behind recommendations. In this work, we revisit the core objective of explainable recommendation: to transparently communicate why an item is recommended by linking user needs to r…

An End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation
Tiantian He, Minzhi Xie, Runtong Li, Xiaoxiao Xu, Jiaqi Yu, Zixiu Wang, Lantao Hu, Han Li, Kun Gai
https://arxiv.org/abs/2508.05093

An End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation
We propose a novel End-to-end Multi-objective Ensemble Ranking framework (EMER) for the multi-objective ensemble ranking module, which is the most critical component of the short video recommendation system. EMER enhances personalization by replacing manually-designed heuristic formulas with an end-to-end modeling paradigm. EMER introduces a meticulously designed loss function to address the fundamental challenge of defining effective supervision for ensemble ranking, where no single ground-tru…

PathWeaver: A High-Throughput Multi-GPU System for Graph-Based Approximate Nearest Neighbor Search
Sukjin Kim, Seongyeon Park, Si Ung Noh, Junguk Hong, Taehee Kwon, Hunseong Lim, Jinho Lee
https://arxiv.org/abs/2507.17094

PathWeaver: A High-Throughput Multi-GPU System for Graph-Based Approximate Nearest Neighbor Search
Graph-based Approximate Nearest Neighbor Search (ANNS) is widely adopted in numerous applications, such as recommendation systems, natural language processing, and computer vision. While recent works on GPU-based acceleration have significantly advanced ANNS performance, the ever-growing scale of datasets now demands efficient multi-GPU solutions. However, the design of existing works overlooks multi-GPU scalability, resulting in naive approaches that treat additional GPUs as a means to extend …

KiseKloset: Comprehensive System For Outfit Retrieval, Recommendation, And Try-On
Thanh-Tung Phan-Nguyen, Khoi-Nguyen Nguyen-Ngoc, Tam V. Nguyen, Minh-Triet Tran, Trung-Nghia Le
https://arxiv.org/abs/2506.23471

KiseKloset: Comprehensive System For Outfit Retrieval, Recommendation, And Try-On
The global fashion e-commerce industry has become integral to people's daily lives, leveraging technological advancements to offer personalized shopping experiences, primarily through recommendation systems that enhance customer engagement through personalized suggestions. To improve customers' experience in online shopping, we propose a novel comprehensive KiseKloset system for outfit retrieval, recommendation, and try-on. We explore two approaches for outfit retrieval: similar item retrieval …

lastfm_aminer: Last.fm social graph
This network contains the social graph of last.fm, a site that provides a streaming radio service, where users can search music and get personalized recommendation. A directed edge (i,j) means that user i follows user j.
This network has 136409 nodes and 1685524 edges.
Tags: Social, Online, Unweighted

lastfm_aminer: Last.fm social graph
This network contains the social graph of last.fm, a site that provides a streaming radio service, where users can search music and get personalized recommendation. A directed edge (i,j) means that user i follows user j.
This network has 136409 nodes and 1685524 edges.
Tags: Social, Online, Unweighted

Personalized Recommendation of Dish and Restaurant Collections on iFood
Fernando F. Granado, Davi A. Bezerra, Iuri Queiroz, Nathan Oliveira, Pedro Fernandes, Bruno Schock
https://arxiv.org/abs/2508.03670

Personalized Recommendation of Dish and Restaurant Collections on iFood
Food delivery platforms face the challenge of helping users navigate vast catalogs of restaurants and dishes to find meals they truly enjoy. This paper presents RED, an automated recommendation system designed for iFood, Latin America's largest on-demand food delivery platform, to personalize the selection of curated food collections displayed to millions of users. Our approach employs a LightGBM classifier that scores collections based on three feature groups: collection characteristics, user-…

lastfm_aminer: Last.fm social graph
This network contains the social graph of last.fm, a site that provides a streaming radio service, where users can search music and get personalized recommendation. A directed edge (i,j) means that user i follows user j.
This network has 136409 nodes and 1685524 edges.
Tags: Social, Online, Unweighted

TransAct V2: Lifelong User Action Sequence Modeling on Pinterest Recommendation
Xue Xia, Saurabh Vishwas Joshi, Kousik Rajesh, Kangnan Li, Yangyi Lu, Nikil Pancha, Dhruvil Deven Badani, Jiajing Xu, Pong Eksombatchai
https://arxiv.org/abs/2506.02267

TransAct V2: Lifelong User Action Sequence Modeling on Pinterest Recommendation
Modeling user action sequences has become a popular focus in industrial recommendation system research, particularly for Click-Through Rate (CTR) prediction tasks. However, industry-scale CTR models often rely on short user sequences, limiting their ability to capture long-term behavior. Additionally, these models typically lack an integrated action-prediction task within a point-wise ranking framework, reducing their predictive power. They also rarely address the infrastructure challenges invo…

This https://arxiv.org/abs/2506.03437 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Quake: Adaptive Indexing for Vector Search
Vector search, the task of finding the k-nearest neighbors of high-dimensional vectors, underpins many machine learning applications, including recommendation systems and information retrieval. However, existing approximate nearest neighbor (ANN) methods perform poorly under dynamic, skewed workloads where data distributions evolve. We introduce Quake, an adaptive indexing system that maintains low latency and high recall in such environments. Quake employs a hierarchical partitioning scheme th…

CityHood: An Explainable Travel Recommender System for Cities and Neighborhoods
Gustavo H Santos, Myriam Delgado, Thiago H Silva
https://arxiv.org/abs/2507.18778 https://…

CityHood: An Explainable Travel Recommender System for Cities and Neighborhoods
We present CityHood, an interactive and explainable recommendation system that suggests cities and neighborhoods based on users' areas of interest. The system models user interests leveraging large-scale Google Places reviews enriched with geographic, socio-demographic, political, and cultural indicators. It provides personalized recommendations at city (Core-Based Statistical Areas - CBSAs) and neighborhood (ZIP code) levels, supported by an explainable technique (LIME) and natural-language ex…

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…

LettinGo: Explore User Profile Generation for Recommendation System
Lu Wang, Di Zhang, Fangkai Yang, Pu Zhao, Jianfeng Liu, Yuefeng Zhan, Hao Sun, Qingwei Lin, Weiwei Deng, Dongmei Zhang, Feng Sun, Qi Zhang
https://arxiv.org/abs/2506.18309

LettinGo: Explore User Profile Generation for Recommendation System
User profiling is pivotal for recommendation systems, as it transforms raw user interaction data into concise and structured representations that drive personalized recommendations. While traditional embedding-based profiles lack interpretability and adaptability, recent advances with large language models (LLMs) enable text-based profiles that are semantically richer and more transparent. However, existing methods often adhere to fixed formats that limit their ability to capture the full diver…

Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System
Yongsen Zheng, Zongxuan Xie, Guohua Wang, Ziyao Liu, Liang Lin, Kwok-Yan Lam
https://arxiv.org/abs/2507.02000

Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System
Unfairness is a well-known challenge in Recommender Systems (RSs), often resulting in biased outcomes that disadvantage users or items based on attributes such as gender, race, age, or popularity. Although some approaches have started to improve fairness recommendation in offline or static contexts, the issue of unfairness often exacerbates over time, leading to significant problems like the Matthew effect, filter bubbles, and echo chambers. To address these challenges, we proposed a novel fram…

Privacy Risks of LLM-Empowered Recommender Systems: An Inversion Attack Perspective
Yubo Wang, Min Tang, Nuo Shen, Shujie Cui, Weiqing Wang
https://arxiv.org/abs/2508.03703 http…

Privacy Risks of LLM-Empowered Recommender Systems: An Inversion Attack Perspective
The large language model (LLM) powered recommendation paradigm has been proposed to address the limitations of traditional recommender systems, which often struggle to handle cold start users or items with new IDs. Despite its effectiveness, this study uncovers that LLM empowered recommender systems are vulnerable to reconstruction attacks that can expose both system and user privacy. To examine this threat, we present the first systematic study on inversion attacks targeting LLM empowered reco…

Compositions of Variant Experts for Integrating Short-Term and Long-Term Preferences
Jaime Hieu Do, Trung-Hoang Le, Hady W. Lauw
https://arxiv.org/abs/2506.23170

Compositions of Variant Experts for Integrating Short-Term and Long-Term Preferences
In the online digital realm, recommendation systems are ubiquitous and play a crucial role in enhancing user experience. These systems leverage user preferences to provide personalized recommendations, thereby helping users navigate through the paradox of choice. This work focuses on personalized sequential recommendation, where the system considers not only a user's immediate, evolving session context, but also their cumulative historical behavior to provide highly relevant and timely recommen…

Fashion-AlterEval: A Dataset for Improved Evaluation of Conversational Recommendation Systems with Alternative Relevant Items
Maria Vlachou
https://arxiv.org/abs/2507.18017 http…

Fashion-AlterEval: A Dataset for Improved Evaluation of Conversational Recommendation Systems with Alternative Relevant Items
In Conversational Recommendation Systems (CRS), a user provides feedback on recommended items at each turn, leading the CRS towards improved recommendations. Due to the need for a large amount of data, a user simulator is employed for both training and evaluation. Such user simulators critique the current retrieved item based on knowledge of a single target item. However, system evaluation in offline settings with simulators is limited by the focus on a single target item and their unlimited pa…

Enhancing Live Broadcast Engagement: A Multi-modal Approach to Short Video Recommendations Using MMGCN and User Preferences
Saeid Aghasoleymani Najafabadi
https://arxiv.org/abs/2506.23085

Enhancing Live Broadcast Engagement: A Multi-modal Approach to Short Video Recommendations Using MMGCN and User Preferences
The purpose of this paper is to explore a multi-modal approach to enhancing live broadcast engagement by developing a short video recommendation system that incorporates Multi-modal Graph Convolutional Networks (MMGCN) with user preferences. In order to provide personalized recommendations tailored to individual interests, the proposed system takes into account user interaction data, video content features, and contextual information. With the aid of a hybrid approach combining collaborative fi…

R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems
Hao Gu, Rui Zhong, Yu Xia, Wei Yang, Chi Lu, Peng Jiang, Kun Gai
https://arxiv.org/abs/2507.17249

R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems
Harnessing Large Language Models (LLMs) for recommendation systems has emerged as a prominent avenue, drawing substantial research interest. However, existing approaches primarily involve basic prompt techniques for knowledge acquisition, which resemble System-1 thinking. This makes these methods highly sensitive to errors in the reasoning path, where even a small mistake can lead to an incorrect inference. To this end, in this paper, we propose $R^{4}$ec, a reasoning, reflection and refinement…

Quake: Adaptive Indexing for Vector Search
Jason Mohoney, Devesh Sarda, Mengze Tang, Shihabur Rahman Chowdhury, Anil Pacaci, Ihab F. Ilyas, Theodoros Rekatsinas, Shivaram Venkataraman
https://arxiv.org/abs/2506.03437

Quake: Adaptive Indexing for Vector Search
Vector search, the task of finding the k-nearest neighbors of high-dimensional vectors, underpins many machine learning applications, including recommendation systems and information retrieval. However, existing approximate nearest neighbor (ANN) methods perform poorly under dynamic, skewed workloads where data distributions evolve. We introduce Quake, an adaptive indexing system that maintains low latency and high recall in such environments. Quake employs a hierarchical partitioning scheme th…

FedFlex: Federated Learning for Diverse Netflix Recommendations
Sven Lankester, Manel Slokom, Gustavo de Carvalho Bertoli, Matias Vizcaino, Emmanuelle Beauxis Aussalet, Laura Hollink
https://arxiv.org/abs/2507.21115

FedFlex: Federated Learning for Diverse Netflix Recommendations
Federated learning is a decentralized approach that enables collaborative model training across multiple devices while preserving data privacy. It has shown significant potential in various domains, including healthcare and personalized recommendation systems. However, most existing work on federated recommendation systems has focused primarily on improving accuracy, with limited attention to fairness and diversity. In this paper, we introduce FedFlex, a federated recommender system for Netflix…

This https://arxiv.org/abs/2505.17549 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

EGA: A Unified End-to-End Generative Framework for Industrial Advertising Systems
Online industrial advertising system is fundamentally constrained by the inefficiency of multi-stage cascaded architectures, which filter out high-potential candidates early and fragment business decision logic across independent modules. Although recent advances in generative recommendation offer end-to-end solutions, they fall short of practical advertising requirements, lacking explicit modeling of bidding, creative selection, allocation mechanism, and payment computation that are essential …

RecPS: Privacy Risk Scoring for Recommender Systems
Jiajie He, Yuechun Gu, Keke Chen
https://arxiv.org/abs/2507.18365 https://arxiv.org/pdf/2507.18365

RecPS: Privacy Risk Scoring for Recommender Systems
Recommender systems (RecSys) have become an essential component of many web applications. The core of the system is a recommendation model trained on highly sensitive user-item interaction data. While privacy-enhancing techniques are actively studied in the research community, the real-world model development still depends on minimal privacy protection, e.g., via controlled access. Users of such systems should have the right to choose \emph{not} to share highly sensitive interactions. However, …

CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems
Haochen Zhang, Tianyi Zhang, Junze Yin, Oren Gal, Anshumali Shrivastava, Vladimir Braverman
https://arxiv.org/abs/2506.19993

CoVE: Compressed Vocabulary Expansion Makes Better LLM-based Recommender Systems
Recommender systems play a pivotal role in providing relevant content to users. With the rapid development of large language models (LLMs), researchers have begun utilizing LLMs to build more powerful recommender systems. However, existing approaches that focus on aligning LLMs with recommendation tasks do not fully leverage their sequential information processing capabilities, leading to suboptimal performance. In this paper, we propose a novel system called compressed vocabulary expansion (…