Tootfinder

Anyone have a good recommendation for a cat net that I can hang in my apartment window?
#Berlin #cats #katzen

This https://arxiv.org/abs/2403.04460 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset
Conversational recommender system is an emerging area that has garnered an increasing interest in the community, especially with the advancements in large language models (LLMs) that enable diverse reasoning over conversational input. Despite the progress, the field has many aspects left to explore. The currently available public datasets for conversational recommendation lack specific user preferences and explanations for recommendations, hindering high-quality recommendations. To address such…

I tend to watch "Hot Ones" on YouTube occasionally and thus the season ending episode with Conan O'Brien came up in my recommendation feed. Just finished watching it and just...
My God. That man is an absolute champion, just completely incredible situational humour and going for it to the max for the sake of the performance. What an episode. Even if you never seen the show before, I think it's worth a watch.

Ice, ice, baby!
#Fernitz

MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation
Wenhao Wu, Jialiang Zhou, Ailong He, Shuguang Han, Jufeng Chen, Bo Zheng
https://arxiv.org/abs/2403.06747

MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation
Compared to business-to-consumer (B2C) e-commerce systems, consumer-to-consumer (C2C) e-commerce platforms usually encounter the limited-stock problem, that is, a product can only be sold one time in a C2C system. This poses several unique challenges for click-through rate (CTR) prediction. Due to limited user interactions for each product (i.e. item), the corresponding item embedding in the CTR model may not easily converge. This makes the conventional sequence modeling based approaches cannot…

"After discounting cohort studies for not being randomised and blinded, and not including any women on the review panel, it is our recommendation that abortion services be refused for under 50s"
If you think the above is bollocks, you'd be right. So is the Cass Report

This https://arxiv.org/abs/2401.10816 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Co-Pilot for Health: Personalized Algorithmic AI Nudging to Improve Health Outcomes
The ability to shape health behaviors of large populations automatically, across wearable types and disease conditions at scale has tremendous potential to improve global health outcomes. We designed and implemented an AI driven platform for digital algorithmic nudging, enabled by a Graph-Neural Network (GNN) based Recommendation System, and granular health behavior data from wearable fitness devices. Here we describe the efficacy results of this platform with its capabilities of personalized a…

Echo Chambers in the Age of Algorithms: An Audit of Twitter's Friend Recommender System
Kayla Duskin, Joseph S. Schafer, Jevin D. West, Emma S. Spiro
https://arxiv.org/abs/2404.06422

Echo Chambers in the Age of Algorithms: An Audit of Twitter's Friend Recommender System
The presence of political misinformation and ideological echo chambers on social media platforms is concerning given the important role that these sites play in the public's exposure to news and current events. Algorithmic systems employed on these platforms are presumed to play a role in these phenomena, but little is known about their mechanisms and effects. In this work, we conduct an algorithmic audit of Twitter's Who-To-Follow friend recommendation system, the first empirical audit that in…

CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System
Yashar Deldjoo, Tommaso di Noia
https://arxiv.org/abs/2403.05668 https://…

CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System
In the evolving landscape of recommender systems, the integration of Large Language Models (LLMs) such as ChatGPT marks a new era, introducing the concept of Recommendation via LLM (RecLLM). While these advancements promise unprecedented personalization and efficiency, they also bring to the fore critical concerns regarding fairness, particularly in how recommendations might inadvertently perpetuate or amplify biases associated with sensitive user attributes. In order to address these concerns,…

Learning Multidimensional Disentangled Representations of Instrumental Sounds for Musical Similarity Assessment
Yuka Hashizume, Li Li, Atsushi Miyashita, Tomoki Toda
https://arxiv.org/abs/2404.06682

Learning Multidimensional Disentangled Representations of Instrumental Sounds for Musical Similarity Assessment
To achieve a flexible recommendation and retrieval system, it is desirable to calculate music similarity by focusing on multiple partial elements of musical pieces and allowing the users to select the element they want to focus on. A previous study proposed using multiple individual networks for calculating music similarity based on each instrumental sound, but it is impractical to use each signal as a query in search systems. Using separated instrumental sounds alternatively resulted in less a…

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

Die concept of "critical ignoring" and the thinking around it that the most recent episode of "Never Post" (yes, this is a podcast recommendation, subscribe now. This is not a drill) explored was really enlightening.
https://pca.st/episode/d82ae6c7-af19-48bf-a00a-…

As Per My Last Podcast Episode... - Never Post
A podcast about and for the internet, hosted by Mike Rugnetta

GTX: A Transactional Graph Data System For HTAP Workloads
Libin Zhou, Walid Aref
https://arxiv.org/abs/2405.01448 https://arxiv.org/p…

GTX: A Transactional Graph Data System For HTAP Workloads
Processing, managing, and analyzing dynamic graphs are the cornerstone in multiple application domains including fraud detection, recommendation system, graph neural network training, etc. This demo presents GTX, a latch-free write-optimized transactional graph data system that supports high throughput read-write transactions while maintaining competitive graph analytics. GTX has a unique latch-free graph storage and a transaction and concurrency control protocol for dynamic power-law graphs. G…

Ok home security camera nerds. I had a leak a while ago (3 years now? Yikes). Fixed leak. Threw plywood to cover ceiling hole because lazy and there is a whole room redo plan. Something has decided that bit of ply is a great 2am dining platform. Mouse? Bigger? Is the weasel in the ceiling? Looking for a recommendation for a small battery wifi cam with dark vision. Ceiling is 2x8 not much height. Like a game trail cam but tiny with close focus.

This https://arxiv.org/abs/2401.16108 has been replaced.
link: https://scholar.google.com/scholar?q=a

Future Impact Decomposition in Request-level Recommendations
In recommender systems, reinforcement learning solutions have shown promising results in optimizing the interaction sequence between users and the system over the long-term performance. For practical reasons, the policy's actions are typically designed as recommending a list of items to handle users' frequent and continuous browsing requests more efficiently. In this list-wise recommendation scenario, the user state is updated upon every request in the corresponding MDP formulation. However, th…

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

This https://arxiv.org/abs/2403.04460 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset
Conversational recommender system is an emerging area that has garnered an increasing interest in the community, especially with the advancements in large language models (LLMs) that enable diverse reasoning over conversational input. Despite the progress, the field has many aspects left to explore. The currently available public datasets for conversational recommendation lack specific user preferences and explanations for recommendations, hindering high-quality recommendations. To address such…

Die concept of "critical ignoring" and the thinking around it that the most recent episode of "Never Post" (yes, this is a podcast recommendation, subscribe now. This is not a drill) explored was really enlightening.
https://pca.st/episode/d82ae6c7-af19-48bf-a00a-…

As Per My Last Podcast Episode... - Never Post
A podcast about and for the internet, hosted by Mike Rugnetta

This https://arxiv.org/abs/2404.18214 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Contrastive Learning Method for Sequential Recommendation based on Multi-Intention Disentanglement
Sequential recommendation is one of the important branches of recommender system, aiming to achieve personalized recommended items for the future through the analysis and prediction of users' ordered historical interactive behaviors. However, along with the growth of the user volume and the increasingly rich behavioral information, how to understand and disentangle the user's interactive multi-intention effectively also poses challenges to behavior prediction and sequential recommendation. In l…

current state: cursing.
in https://www.europarl.europa.eu/doceo/document/A-9-2023-0404_EN.html
after para 1 point (as) the next point is (au) and not as expected (at).
the inventiveness of inconsistencies in the EP is infinite.
*sc…

This https://arxiv.org/abs/2204.05436 has been replaced.
link: https://scholar.google.com/scholar?q=a

Heterogeneous Acceleration Pipeline for Recommendation System Training
Recommendation models rely on deep learning networks and large embedding tables, resulting in computationally and memory-intensive processes. These models are typically trained using hybrid CPU-GPU or GPU-only configurations. The hybrid mode combines the GPU's neural network acceleration with the CPUs' memory storage and supply for embedding tables but may incur significant CPU-to-GPU transfer time. In contrast, the GPU-only mode utilizes High Bandwidth Memory (HBM) across multiple GPUs for sto…

Public Comments on Recommendation for Block Cipher Modes of Operation: the XTS-AES Mode for Confidentiality on Storage Devices
https://csrc.nist.gov/csrc/media/Projects/…

If I haven't recommended Tarsnap, or @… book on Tarsnap to you, here is my recommendation. Seriously, sometimes I forget I even have it, until I have to recover something. You can't ask for a better service than that. (ok, sometimes I run out of credit and then I remember too, but seriously, its great)

This https://arxiv.org/abs/2307.14551 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

How to Train Your YouTube Recommender to Avoid Unwanted Videos
YouTube provides features for users to indicate disinterest when presented with unwanted recommendations, such as the "Not interested" and "Don't recommend channel" buttons. These buttons purportedly allow the user to correct "mistakes" made by the recommendation system. Yet, relatively little is known about the empirical efficacy of these buttons. Neither is much known about users' awareness of and confidence in them. To address these gaps, we simulated YouTube users with sock puppet agents. E…

A reminder to folk that #FediHire is a thing and that there are positions available.
Still, it's also important to take time if you can to process this.
You have value. You have skill.
(Also, REALLY good time to add notes of recommendation to folks profiles on LinkedIn and other professional services.)

This https://arxiv.org/abs/2402.13959 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Retention Induced Biases in a Recommendation System with Heterogeneous Users
I examine a conceptual model of a recommendation system (RS) with user inflow and churn dynamics. When inflow and churn balance out, the user distribution reaches a steady state. Changing the recommendation algorithm alters the steady state and creates a transition period. During this period, the RS behaves differently from its new steady state. In particular, A/B experiment metrics obtained in transition periods are biased indicators of the RS's long term performance. Scholars and practitioner…

This https://arxiv.org/abs/2210.14306 has been replaced.
link: https://scholar.google.com/scholar?q=a

Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming
Code-recommendation systems, such as Copilot and CodeWhisperer, have the potential to improve programmer productivity by suggesting and auto-completing code. However, to fully realize their potential, we must understand how programmers interact with these systems and identify ways to improve that interaction. To seek insights about human-AI collaboration with code recommendations systems, we studied GitHub Copilot, a code-recommendation system used by millions of programmers daily. We developed…

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

This https://arxiv.org/abs/2401.12578 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

ToDA: Target-oriented Diffusion Attacker against Recommendation System
Recommendation systems (RS) have become indispensable tools for web services to address information overload, thus enhancing user experiences and bolstering platforms' revenues. However, with their increasing ubiquity, security concerns have also emerged. As the public accessibility of RS, they are susceptible to specific malicious attacks where adversaries can manipulate user profiles, leading to biased recommendations. Recent research often integrates additional modules using generative model…

Cache-Aware Reinforcement Learning in Large-Scale Recommender Systems
Xiaoshuang Chen, Gengrui Zhang, Yao Wang, Yulin Wu, Shuo Su, Kaiqiao Zhan, Ben Wang
https://arxiv.org/abs/2404.14961

Cache-Aware Reinforcement Learning in Large-Scale Recommender Systems
Modern large-scale recommender systems are built upon computation-intensive infrastructure and usually suffer from a huge difference in traffic between peak and off-peak periods. In peak periods, it is challenging to perform real-time computation for each request due to the limited budget of computational resources. The recommendation with a cache is a solution to this problem, where a user-wise result cache is used to provide recommendations when the recommender system cannot afford a real-tim…

This https://arxiv.org/abs/2308.13249 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System
Short-video recommendation is one of the most important recommendation applications in today's industrial information systems. Compared with other recommendation tasks, the enormous amount of feedback is the most typical characteristic. Specifically, in short-video recommendation, the easiest-to-collect user feedback is the skipping behavior, which leads to two critical challenges for the recommendation model. First, the skipping behavior reflects implicit user preferences, and thus, it is chal…

Public Comments on Recommendation for Block Cipher Modes of Operation: the XTS-AES Mode for Confidentiality on Storage Devices
https://csrc.nist.gov/csrc/media/Projects/…

Movie recommendation for tonight? I just don't wanna work another evening. (Nothing from any cinematic universe or with a number postfixed)

Negotiating the Shared Agency between Humans & AI in the Recommender System
Mengke Wu (Mia), Weizi Liu (Mia), Yanyun (Mia), Wang, Mike Zhengyu Yao
https://arxiv.org/abs/2403.15919

Negotiating the Shared Agency between Humans & AI in the Recommender System
Smart recommendation algorithms have revolutionized information dissemination, enhancing efficiency and reshaping content delivery across various domains. However, concerns about user agency have arisen due to the inherent opacity (information asymmetry) and the nature of one-way output (power asymmetry) on algorithms. While both issues have been criticized by scholars via advocating explainable AI (XAI) and human-AI collaborative decision-making (HACD), few research evaluates their integrated …

This https://arxiv.org/abs/2402.18240 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Prospect Personalized Recommendation on Large Language Model-based Agent Platform
The new kind of Agent-oriented information system, exemplified by GPTs, urges us to inspect the information system infrastructure to support Agent-level information processing and to adapt to the characteristics of Large Language Model (LLM)-based Agents, such as interactivity. In this work, we envisage the prospect of the recommender system on LLM-based Agent platforms and introduce a novel recommendation paradigm called Rec4Agentverse, comprised of Agent Items and Agent Recommender. Rec4Agent…

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

The 2nd Workshop on Recommendation with Generative Models
Wenjie Wang, Yang Zhang, Xinyu Lin, Fuli Feng, Weiwen Liu, Yong Liu, Xiangyu Zhao, Wayne Xin Zhao, Yang Song, Xiangnan He
https://arxiv.org/abs/2403.04399

The 2nd Workshop on Recommendation with Generative Models
The rise of generative models has driven significant advancements in recommender systems, leaving unique opportunities for enhancing users' personalized recommendations. This workshop serves as a platform for researchers to explore and exchange innovative concepts related to the integration of generative models into recommender systems. It primarily focuses on five key perspectives: (i) improving recommender algorithms, (ii) generating personalized content, (iii) evolving the user-system intera…

This https://arxiv.org/abs/2306.17256 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendations
Recommendation systems help users find matched items based on their previous behaviors. Personalized recommendation becomes challenging in the absence of historical user-item interactions, a practical problem for startups known as the system cold-start recommendation. While existing research addresses cold-start issues for either users or items, we still lack solutions for system cold-start scenarios. To tackle the problem, we propose PromptRec, a simple but effective approach based on in-conte…

This https://arxiv.org/abs/2401.12578 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

ToDA: Target-oriented Diffusion Attacker against Recommendation System
Recommendation systems (RS) have become indispensable tools for web services to address information overload, thus enhancing user experiences and bolstering platforms' revenues. However, with their increasing ubiquity, security concerns have also emerged. As the public accessibility of RS, they are susceptible to specific malicious attacks where adversaries can manipulate user profiles, leading to biased recommendations. Recent research often integrates additional modules using generative model…

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

Quantixar: High-performance Vector Data Management System
Gulshan Yadav, RahulKumar Yadav, Mansi Viramgama, Mayank Viramgama, Apeksha Mohite
https://arxiv.org/abs/2403.12583

Quantixar: High-performance Vector Data Management System
Traditional database management systems need help efficiently represent and querying the complex, high-dimensional data prevalent in modern applications. Vector databases offer a solution by storing data as numerical vectors within a multi-dimensional space. This enables similarity-based search and analysis, such as image retrieval, recommendation engine generation, and natural language processing. This paper introduces Quantixar, a vector database project designed for efficiency in high-dimens…

Stability of Explainable Recommendation
Sairamvinay Vijayaraghavan, Prasant Mohapatra
https://arxiv.org/abs/2405.01849 https://arxiv.…

Stability of Explainable Recommendation
Explainable Recommendation has been gaining attention over the last few years in industry and academia. Explanations provided along with recommendations in a recommender system framework have many uses: particularly reasoning why a suggestion is provided and how well an item aligns with a user's personalized preferences. Hence, explanations can play a huge role in influencing users to purchase products. However, the reliability of the explanations under varying scenarios has not been strictly v…

A Model-based Multi-Agent Personalized Short-Video Recommender System
Peilun Zhou, Xiaoxiao Xu, Lantao Hu, Han Li, Peng Jiang
https://arxiv.org/abs/2405.01847

A Model-based Multi-Agent Personalized Short-Video Recommender System
Recommender selects and presents top-K items to the user at each online request, and a recommendation session consists of several sequential requests. Formulating a recommendation session as a Markov decision process and solving it by reinforcement learning (RL) framework has attracted increasing attention from both academic and industry communities. In this paper, we propose a RL-based industrial short-video recommender ranking framework, which models and maximizes user watch-time in an enviro…

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

Deutsche Unternehmen wie Bayer und BASF exportieren weiterhin Tausende Tonnen von gefährlichen Pestiziden in alle Welt. Pestizide, die in der EU - aus gutem Grund - längst verboten sind. Diese Agrargifte können Embryonen schädigen, Krebs verursachen und sind schon in kleinen Mengen tödlich. Die Ampel-Regierung hatte im Koalitionsvertrag versprochen, diesen gefährlichen Doppelstandards ein Ende zu setzen und die giftigen Exporte rechtlich zu stoppen. Ein Entwurf liegt schon seit Monaten vor. Doch nun blockieren Teile der Bundesregierung offenbar das Verbot. Wir fordern von Ihnen, Landwirtschaftsminister Cem Özdemir, Wirtschaftsminister Robert Habeck und Justizminister Dr. Marco Buschmann, endlich das Exportverbot für hochgiftige Pestizide durchzusetzen!
Bitte Petition unterzeichnen und boosten 🔁
Danke
https://weact.campact.de/petitions/giftexporte-stoppen-4?source=rawlink&utm_medium=recommendation&utm_source=rawlink&share=f922bd22-5559-4888-8461-27ad27acdc37

This https://arxiv.org/abs/2208.03367 has been replaced.
link: https://scholar.google.com/scholar?q=a

Sublinear Time Algorithm for Online Weighted Bipartite Matching
Online bipartite matching is a fundamental problem in online algorithms. The goal is to match two sets of vertices to maximize the sum of the edge weights, where for one set of vertices, each vertex and its corresponding edge weights appear in a sequence. Currently, in the practical recommendation system or search engine, the weights are decided by the inner product between the deep representation of a user and the deep representation of an item. The standard online matching needs to pay $nd$ t…

This https://arxiv.org/abs/2306.01163 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

A Multi-Modal Latent-Features based Service Recommendation System for the Social Internet of Things
The Social Internet of Things (SIoT), is revolutionizing how we interact with our everyday lives. By adding the social dimension to connecting devices, the SIoT has the potential to drastically change the way we interact with smart devices. This connected infrastructure allows for unprecedented levels of convenience, automation, and access to information, allowing us to do more with less effort. However, this revolutionary new technology also brings an eager need for service recommendation syst…

Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention
Ziru Liu, Shuchang Liu, Zijian Zhang, Qingpeng Cai, Xiangyu Zhao, Kesen Zhao, Lantao Hu, Peng Jiang, Kun Gai
https://arxiv.org/abs/2404.03637

Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention
In the landscape of Recommender System (RS) applications, reinforcement learning (RL) has recently emerged as a powerful tool, primarily due to its proficiency in optimizing long-term rewards. Nevertheless, it suffers from instability in the learning process, stemming from the intricate interactions among bootstrapping, off-policy training, and function approximation. Moreover, in multi-reward recommendation scenarios, designing a proper reward setting that reconciles the inner dynamics of vari…

Robust Explainable Recommendation
Sairamvinay Vijayaraghavan, Prasant Mohapatra
https://arxiv.org/abs/2405.01855 https://arxiv.org/pd…

Robust Explainable Recommendation
Explainable Recommender Systems is an important field of study which provides reasons behind the suggested recommendations. Explanations with recommender systems are useful for developers while debugging anomalies within the system and for consumers while interpreting the model's effectiveness in capturing their true preferences towards items. However, most of the existing state-of-the-art (SOTA) explainable recommenders could not retain their explanation capability under noisy circumstances an…

This https://arxiv.org/abs/2403.15919 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Negotiating the Shared Agency between Humans & AI in the Recommender System
Smart recommendation algorithms have revolutionized information dissemination, enhancing efficiency and reshaping content delivery across various domains. However, concerns about user agency have arisen due to the inherent opacity (information asymmetry) and the nature of one-way output (power asymmetry) on algorithms. While both issues have been criticized by scholars via advocating explainable AI (XAI) and human-AI collaborative decision-making (HACD), few research evaluates their integrated …

Item-side Fairness of Large Language Model-based Recommendation System
Meng Jiang, Keqin Bao, Jizhi Zhang, Wenjie Wang, Zhengyi Yang, Fuli Feng, Xiangnan He
https://arxiv.org/abs/2402.15215

Item-side Fairness of Large Language Model-based Recommendation System
Recommendation systems for Web content distribution intricately connect to the information access and exposure opportunities for vulnerable populations. The emergence of Large Language Models-based Recommendation System (LRS) may introduce additional societal challenges to recommendation systems due to the inherent biases in Large Language Models (LLMs). From the perspective of item-side fairness, there remains a lack of comprehensive investigation into the item-side fairness of LRS given the u…

Item-side Fairness of Large Language Model-based Recommendation System
Meng Jiang, Keqin Bao, Jizhi Zhang, Wenjie Wang, Zhengyi Yang, Fuli Feng, Xiangnan He
https://arxiv.org/abs/2402.15215

Item-side Fairness of Large Language Model-based Recommendation System
Recommendation systems for Web content distribution intricately connect to the information access and exposure opportunities for vulnerable populations. The emergence of Large Language Models-based Recommendation System (LRS) may introduce additional societal challenges to recommendation systems due to the inherent biases in Large Language Models (LLMs). From the perspective of item-side fairness, there remains a lack of comprehensive investigation into the item-side fairness of LRS given the u…

A Novel Behavior-Based Recommendation System for E-commerce
Reza Barzegar Nozari, Mahdi Divsalar, Sepehr Akbarzadeh Abkenar, Mohammadreza Fadavi Amiri, Ali Divsalar
https://arxiv.org/abs/2403.18536

A Novel Behavior-Based Recommendation System for E-commerce
The majority of existing recommender systems rely on user ratings, which are limited by the lack of user collaboration and the sparsity problem. To address these issues, this study proposes a behavior-based recommender system that leverages customers' natural behaviors, such as browsing and clicking, on e-commerce platforms. The proposed recommendation system involves clustering active customers, determining neighborhoods, collecting similar users, calculating product reputation based on simila…

A Novel Behavior-Based Recommendation System for E-commerce
Reza Barzegar Nozari, Mahdi Divsalar, Sepehr Akbarzadeh Abkenar, Mohammadreza Fadavi Amiri, Ali Divsalar
https://arxiv.org/abs/2403.18536

A Novel Behavior-Based Recommendation System for E-commerce
The majority of existing recommender systems rely on user ratings, which are limited by the lack of user collaboration and the sparsity problem. To address these issues, this study proposes a behavior-based recommender system that leverages customers' natural behaviors, such as browsing and clicking, on e-commerce platforms. The proposed recommendation system involves clustering active customers, determining neighborhoods, collecting similar users, calculating product reputation based on simila…

This https://arxiv.org/abs/2208.03367 has been replaced.
link: https://scholar.google.com/scholar?q=a

Sublinear Time Algorithm for Online Weighted Bipartite Matching
Online bipartite matching is a fundamental problem in online algorithms. The goal is to match two sets of vertices to maximize the sum of the edge weights, where for one set of vertices, each vertex and its corresponding edge weights appear in a sequence. Currently, in the practical recommendation system or search engine, the weights are decided by the inner product between the deep representation of a user and the deep representation of an item. The standard online matching needs to pay $nd$ t…

Prospect Personalized Recommendation on Large Language Model-based Agent Platform
Jizhi Zhang, Keqin Bao, Wenjie Wang, Yang Zhang, Wentao Shi, Wanhong Xu, Fuli Feng, Tat-Seng Chua
https://arxiv.org/abs/2402.18240

Prospect Personalized Recommendation on Large Language Model-based Agent Platform
The new kind of Agent-oriented information system, exemplified by GPTs, urges us to inspect the information system infrastructure to support Agent-level information processing and to adapt to the characteristics of Large Language Model (LLM)-based Agents, such as interactivity. In this work, we envisage the prospect of the recommender system on LLM-based Agent platforms and introduce a novel recommendation paradigm called Rec4Agentverse, comprised of Agent Items and Agent Recommender. Rec4Agent…

This https://arxiv.org/abs/2301.04945 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donations
Short-format videos have exploded on platforms like TikTok, Instagram, and YouTube. Despite this, the research community lacks large-scale empirical studies into how people engage with short-format videos and the role of recommendation systems that offer endless streams of such content. In this work, we analyze user engagement on TikTok using data we collect via a data donation system that allows TikTok users to donate their data. We recruited 347 TikTok users and collected 9.2M TikTok video re…

Implementation of Recommendation Algorithm based on Recommendation Sessions in E-commerce IT System
Micha{\l} Malinowski
https://arxiv.org/abs/2402.08275 h…

Implementation of Recommendation Algorithm based on Recommendation Sessions in E-commerce IT System
This paper presents a study on the implementation of the author's Algorithm of Recommendation Sessions (ARS) in an operational e-commerce information system and analyses the basic parameters of the resulting recommendation system. It begins with a synthetic overview of recommendation systems, followed by a presentation of the proprietary ARS algorithm, which is based on recommendation sessions. A mathematical model of the recommendation session, constructed using graph and network theory, serve…

Advanced Academic Team Worker Recommendation Models
Mi Wu
https://arxiv.org/abs/2402.16876 https://arxiv.org/pdf/2402.16876

Advanced Academic Team Worker Recommendation Models
Collaborator recommendation is an important task in academic domain. Most of the existing approaches have the assumption that the recommendation system only need to recommend a specific researcher for the task. However, academic successes can be owed to productive collaboration of a whole academic team. In this work, we propose a new task: academic team worker recommendation: with a given status: student, assistant professor or prime professor, research interests and specific task, we can recom…

Personalized Negative Reservoir for Incremental Learning in Recommender Systems
Antonios Valkanas, Yuening Wang, Yingxue Zhang, Mark Coates
https://arxiv.org/abs/2403.03993

Personalized Negative Reservoir for Incremental Learning in Recommender Systems
Recommender systems have become an integral part of online platforms. Every day the volume of training data is expanding and the number of user interactions is constantly increasing. The exploration of larger and more expressive models has become a necessary pursuit to improve user experience. However, this progression carries with it an increased computational burden. In commercial settings, once a recommendation system model has been trained and deployed it typically needs to be updated frequ…

Contrastive Learning Method for Sequential Recommendation based on Multi-Intention Disentanglement
Zeyu Hu, Yuzhi Xiao, Tao Huang, Xuanrong Huo
https://arxiv.org/abs/2404.18214

Contrastive Learning Method for Sequential Recommendation based on Multi-Intention Disentanglement
Sequential recommendation is one of the important branches of recommender system, aiming to achieve personalized recommended items for the future through the analysis and prediction of users' ordered historical interactive behaviors. However, along with the growth of the user volume and the increasingly rich behavioral information, how to understand and disentangle the user's interactive multi-intention effectively also poses challenges to behavior prediction and sequential recommendation. In l…

Retention Induced Biases in a Recommendation System with Heterogeneous Users
Shichao Ma
https://arxiv.org/abs/2402.13959 https://arxi…

Retention Induced Biases in a Recommendation System with Heterogeneous Users
I examine a conceptual model of a recommendation system (RS) with user inflow and churn dynamics. When inflow and churn balance out, the user distribution reaches a steady state. Changing the recommendation algorithm alters the steady state and creates a transition period. During this period, the RS behaves differently from its new steady state. In particular, A/B experiment metrics obtained in transition periods are biased indicators of the RS's long term performance. Scholars and practitioner…

This https://arxiv.org/abs/2308.14296 has been replaced.
link: https://scholar.google.com/scholar?q=a

RecMind: Large Language Model Powered Agent For Recommendation
While the recommendation system (RS) has advanced significantly through deep learning, current RS approaches usually train and fine-tune models on task-specific datasets, limiting their generalizability to new recommendation tasks and their ability to leverage external knowledge due to model scale and data size constraints. Thus, we designed an LLM-powered autonomous recommender agent, RecMind, which is capable of leveraging external knowledge, utilizing tools with careful planning to provide z…

This https://arxiv.org/abs/2305.12650 has been replaced.
link: https://scholar.google.com/scholar?q=a

When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions
Federated recommendation system usually trains a global model on the server without direct access to users' private data on their own devices. However, this separation of the recommendation model and users' private data poses a challenge in providing quality service, particularly when it comes to new items, namely cold-start recommendations in federated settings. This paper introduces a novel method called Item-aligned Federated Aggregation (IFedRec) to address this challenge. It is the first r…

Towards Robust Recommendation: A Review and an Adversarial Robustness Evaluation Library
Lei Cheng, Xiaowen Huang, Jitao Sang, Jian Yu
https://arxiv.org/abs/2404.17844

Towards Robust Recommendation: A Review and an Adversarial Robustness Evaluation Library
Recently, recommender system has achieved significant success. However, due to the openness of recommender systems, they remain vulnerable to malicious attacks. Additionally, natural noise in training data and issues such as data sparsity can also degrade the performance of recommender systems. Therefore, enhancing the robustness of recommender systems has become an increasingly important research topic. In this survey, we provide a comprehensive overview of the robustness of recommender system…

RE-RecSys: An End-to-End system for recommending properties in Real-Estate domain
Venkatesh C, Harshit Oberoi, Anil Goyal, Nikhil Sikka
https://arxiv.org/abs/2404.16553

RE-RecSys: An End-to-End system for recommending properties in Real-Estate domain
We propose an end-to-end real-estate recommendation system, RE-RecSys, which has been productionized in real-world industry setting. We categorize any user into 4 categories based on available historical data: i) cold-start users; ii) short-term users; iii) long-term users; and iv) short-long term users. For cold-start users, we propose a novel rule-based engine that is based on the popularity of locality and user preferences. For short-term users, we propose to use content-filtering model whic…

Ensure Timeliness and Accuracy: A Novel Sliding Window Data Stream Paradigm for Live Streaming Recommendation
Fengqi Liang, Baigong Zheng, Liqin Zhao, Guorui Zhou, Qian Wang, Yanan Niu
https://arxiv.org/abs/2402.14399

Ensure Timeliness and Accuracy: A Novel Sliding Window Data Stream Paradigm for Live Streaming Recommendation
Live streaming recommender system is specifically designed to recommend real-time live streaming of interest to users. Due to the dynamic changes of live content, improving the timeliness of the live streaming recommender system is a critical problem. Intuitively, the timeliness of the data determines the upper bound of the timeliness that models can learn. However, none of the previous works addresses the timeliness problem of the live streaming recommender system from the perspective of data …

This https://arxiv.org/abs/2404.15678 has been replaced.
link: https://scholar.google.com/scholar?q=a

Retrieval and Distill: A Temporal Data Shift-Free Paradigm for Online Recommendation System
Current recommendation systems are significantly affected by a serious issue of temporal data shift, which is the inconsistency between the distribution of historical data and that of online data. Most existing models focus on utilizing updated data, overlooking the transferable, temporal data shift-free information that can be learned from shifting data. We propose the Temporal Invariance of Association theorem, which suggests that given a fixed search space, the relationship between the data …

Enriching User Shopping History: Empowering E-commerce with a Hierarchical Recommendation System
Irem Islek, Sule Gunduz Oguducu
https://arxiv.org/abs/2403.12096

Enriching User Shopping History: Empowering E-commerce with a Hierarchical Recommendation System
Recommendation systems can provide accurate recommendations by analyzing user shopping history. A richer user history results in more accurate recommendations. However, in real applications, users prefer e-commerce platforms where the item they seek is at the lowest price. In other words, most users shop from multiple e-commerce platforms simultaneously; different parts of the user's shopping history are shared between different e-commerce platforms. Consequently, we assume in this study that a…

Distributionally Robust Graph-based Recommendation System
Bohao Wang, Jiawei Chen, Changdong Li, Sheng Zhou, Qihao Shi, Yang Gao, Yan Feng, Chun Chen, Can Wang
https://arxiv.org/abs/2402.12994

Distributionally Robust Graph-based Recommendation System
With the capacity to capture high-order collaborative signals, Graph Neural Networks (GNNs) have emerged as powerful methods in Recommender Systems (RS). However, their efficacy often hinges on the assumption that training and testing data share the same distribution (a.k.a. IID assumption), and exhibits significant declines under distribution shifts. Distribution shifts commonly arises in RS, often attributed to the dynamic nature of user preferences or ubiquitous biases during data collection…

This https://arxiv.org/abs/2204.11602 has been replaced.
link: https://scholar.google.com/scholar?q=a

Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach
Recently, Deep Neural Networks (DNNs) have been widely introduced into Collaborative Filtering (CF) to produce more accurate recommendation results due to their capability of capturing the complex nonlinear relationships between items and users.However, the DNNs-based models usually suffer from high computational complexity, i.e., consuming very long training time and storing huge amount of trainable parameters. To address these problems, we propose a new broad recommender system called Broad C…

This https://arxiv.org/abs/2308.14296 has been replaced.
link: https://scholar.google.com/scholar?q=a

RecMind: Large Language Model Powered Agent For Recommendation
While the recommendation system (RS) has advanced significantly through deep learning, current RS approaches usually train and fine-tune models on task-specific datasets, limiting their generalizability to new recommendation tasks and their ability to leverage external knowledge due to model scale and data size constraints. Thus, we designed an LLM-powered autonomous recommender agent, RecMind, which is capable of leveraging external knowledge, utilizing tools with careful planning to provide z…

This https://arxiv.org/abs/2404.15678 has been replaced.
link: https://scholar.google.com/scholar?q=a

Retrieval and Distill: A Temporal Data Shift-Free Paradigm for Online Recommendation System
Current recommendation systems are significantly affected by a serious issue of temporal data shift, which is the inconsistency between the distribution of historical data and that of online data. Most existing models focus on utilizing updated data, overlooking the transferable, temporal data shift-free information that can be learned from shifting data. We propose the Temporal Invariance of Association theorem, which suggests that given a fixed search space, the relationship between the data …

This https://arxiv.org/abs/2404.10876 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Course Recommender Systems Need to Consider the Job Market
Current course recommender systems primarily leverage learner-course interactions, course content, learner preferences, and supplementary course details like instructor, institution, ratings, and reviews, to make their recommendation. However, these systems often overlook a critical aspect: the evolving skill demand of the job market. This paper focuses on the perspective of academic researchers, working in collaboration with the industry, aiming to develop a course recommender system that inco…

Context-based Fast Recommendation Strategy for Long User Behavior Sequence in Meituan Waimai
Zhichao Feng, Junjiie Xie, Kaiyuan Li, Yu Qin, Pengfei Wang, Qianzhong Li, Bin Yin, Xiang Li, Wei Lin, Shangguang Wang
https://arxiv.org/abs/2403.12566

Context-based Fast Recommendation Strategy for Long User Behavior Sequence in Meituan Waimai
In the recommender system of Meituan Waimai, we are dealing with ever-lengthening user behavior sequences, which pose an increasing challenge to modeling user preference effectively. Existing sequential recommendation models often fail to capture long-term dependencies or are too complex, complicating the fulfillment of Meituan Waimai's unique business needs. To better model user interests, we consider selecting relevant sub-sequences from users' extensive historical behaviors based on their pr…

Revealing and Utilizing In-group Favoritism for Graph-based Collaborative Filtering
Hoin Jung, Hyunsoo Cho, Myungje Choi, Joowon Lee, Jung Ho Park, Myungjoo Kang
https://arxiv.org/abs/2404.17598

Revealing and Utilizing In-group Favoritism for Graph-based Collaborative Filtering
When it comes to a personalized item recommendation system, It is essential to extract users' preferences and purchasing patterns. Assuming that users in the real world form a cluster and there is common favoritism in each cluster, in this work, we introduce Co-Clustering Wrapper (CCW). We compute co-clusters of users and items with co-clustering algorithms and add CF subnetworks for each cluster to extract the in-group favoritism. Combining the features from the networks, we obtain rich and un…

This https://arxiv.org/abs/2306.17256 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Could Small Language Models Serve as Recommenders? Towards Data-centric Cold-start Recommendations
Recommendation systems help users find information that matches their interests based on their historical behaviors. However, generating personalized recommendations becomes challenging in the absence of historical user-item interactions, a practical problem for startups known as the system cold-start recommendation. Current research tackles user or item cold-start scenarios but lacks solutions for system cold-start. To tackle the problem, we initially propose PromptRec, a simple but effective …

This https://arxiv.org/abs/2403.06747 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation
Compared to business-to-consumer (B2C) e-commerce systems, consumer-to-consumer (C2C) e-commerce platforms usually encounter the limited-stock problem, that is, a product can only be sold one time in a C2C system. This poses several unique challenges for click-through rate (CTR) prediction. Due to limited user interactions for each product (i.e. item), the corresponding item embedding in the CTR model may not easily converge. This makes the conventional sequence modeling based approaches cannot…

This https://arxiv.org/abs/2307.09985 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Our Model Achieves Excellent Performance on MovieLens: What Does it Mean?
A typical benchmark dataset for recommender system (RecSys) evaluation consists of user-item interactions generated on a platform within a time period. The interaction generation mechanism partially explains why a user interacts with (e.g.,like, purchase, rate) an item, and the context of when a particular interaction happened. In this study, we conduct a meticulous analysis on the MovieLens dataset and explain the potential impact on using the dataset for evaluating recommendation algorithms. …

This https://arxiv.org/abs/2402.12994 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Distributionally Robust Graph-based Recommendation System
With the capacity to capture high-order collaborative signals, Graph Neural Networks (GNNs) have emerged as powerful methods in Recommender Systems (RS). However, their efficacy often hinges on the assumption that training and testing data share the same distribution (a.k.a. IID assumption), and exhibits significant declines under distribution shifts. Distribution shifts commonly arises in RS, often attributed to the dynamic nature of user preferences or ubiquitous biases during data collection…

This https://arxiv.org/abs/2311.14968 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Hide Your Model: A Parameter Transmission-free Federated Recommender System
With the growing concerns regarding user data privacy, Federated Recommender System (FedRec) has garnered significant attention recently due to its privacy-preserving capabilities. Existing FedRecs generally adhere to a learning protocol in which a central server shares a global recommendation model with clients, and participants achieve collaborative learning by frequently communicating the model's public parameters. Nevertheless, this learning framework has two drawbacks that limit its practi…

This https://arxiv.org/abs/2403.06747 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation
Compared to business-to-consumer (B2C) e-commerce systems, consumer-to-consumer (C2C) e-commerce platforms usually encounter the limited-stock problem, that is, a product can only be sold one time in a C2C system. This poses several unique challenges for click-through rate (CTR) prediction. Due to limited user interactions for each product (i.e. item), the corresponding item embedding in the CTR model may not easily converge. This makes the conventional sequence modeling based approaches cannot…

This https://arxiv.org/abs/2403.06747 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation
Compared to business-to-consumer (B2C) e-commerce systems, consumer-to-consumer (C2C) e-commerce platforms usually encounter the limited-stock problem, that is, a product can only be sold one time in a C2C system. This poses several unique challenges for click-through rate (CTR) prediction. Due to limited user interactions for each product (i.e. item), the corresponding item embedding in the CTR model may not easily converge. This makes the conventional sequence modeling based approaches cannot…

This https://arxiv.org/abs/2309.07705 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation
Large foundational models, through upstream pre-training and downstream fine-tuning, have achieved immense success in the broad AI community due to improved model performance and significant reductions in repetitive engineering. By contrast, the transferable one-for-all models in the recommender system field, referred to as TransRec, have made limited progress. The development of TransRec has encountered multiple challenges, among which the lack of large-scale, high-quality transfer learning re…

This https://arxiv.org/abs/2309.07705 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation
Large foundational models, through upstream pre-training and downstream fine-tuning, have achieved immense success in the broad AI community due to improved model performance and significant reductions in repetitive engineering. By contrast, the transferable one-for-all models in the recommender system field, referred to as TransRec, have made limited progress. The development of TransRec has encountered multiple challenges, among which the lack of large-scale, high-quality transfer learning re…

This https://arxiv.org/abs/2307.09985 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Our Model Achieves Excellent Performance on MovieLens: What Does it Mean?
A typical benchmark dataset for recommender system (RecSys) evaluation consists of user-item interactions generated on a platform within a time period. The interaction generation mechanism partially explains why a user interacts with (e.g.,like, purchase, rate) an item, and the context of when a particular interaction happened. In this study, we conduct a meticulous analysis on the MovieLens dataset and explain the potential impact on using the dataset for evaluating recommendation algorithms. …

This https://arxiv.org/abs/2307.09985 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Our Model Achieves Excellent Performance on MovieLens: What Does it Mean?
A typical benchmark dataset for recommender system (RecSys) evaluation consists of user-item interactions generated on a platform within a time period. The interaction generation mechanism partially explains why a user interacts with (e.g.,like, purchase, rate) an item, and the context of when a particular interaction happened. In this study, we conduct a meticulous analysis on the MovieLens dataset and explain the potential impact on using the dataset for evaluating recommendation algorithms. …

Course Recommender Systems Need to Consider the Job Market
Jibril Frej, Anna Dai, Syrielle Montariol, Antoine Bosselut, Tanja K\"aser
https://arxiv.org/abs/2404.10876

Course Recommender Systems Need to Consider the Job Market
Current course recommender systems primarily leverage learner-course interactions, course content, learner preferences, and supplementary course details like instructor, institution, ratings, and reviews, to make their recommendation. However, these systems often overlook a critical aspect: the evolving skill demand of the job market. This paper focuses on the perspective of academic researchers, working in collaboration with the industry, aiming to develop a course recommender system that inco…

This https://arxiv.org/abs/2403.00895 has been replaced.
link: https://scholar.google.com/scholar?q=a

End-to-end Graph-Sequential Representation Learning for Accurate Recommendations
Recent recommender system advancements have focused on developing sequence-based and graph-based approaches. Both approaches proved useful in modeling intricate relationships within behavioral data, leading to promising outcomes in personalized ranking and next-item recommendation tasks while maintaining good scalability. However, they capture very different signals from data. While the former approach represents users directly through ordered interactions with recent items, the latter aims to …

This https://arxiv.org/abs/2403.00895 has been replaced.
link: https://scholar.google.com/scholar?q=a

End-to-end Graph-Sequential Representation Learning for Accurate Recommendations
Recent recommender system advancements have focused on developing sequence-based and graph-based approaches. Both approaches proved useful in modeling intricate relationships within behavioral data, leading to promising outcomes in personalized ranking and next-item recommendation tasks while maintaining good scalability. However, they capture very different signals from data. While the former approach represents users directly through ordered interactions with recent items, the latter aims to …

This https://arxiv.org/abs/2403.06747 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation
Compared to business-to-consumer (B2C) e-commerce systems, consumer-to-consumer (C2C) e-commerce platforms usually encounter the limited-stock problem, that is, a product can only be sold one time in a C2C system. This poses several unique challenges for click-through rate (CTR) prediction. Due to limited user interactions for each product (i.e. item), the corresponding item embedding in the CTR model may not easily converge. This makes the conventional sequence modeling based approaches cannot…

This https://arxiv.org/abs/2403.06747 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation
Compared to business-to-consumer (B2C) e-commerce systems, consumer-to-consumer (C2C) e-commerce platforms usually encounter the limited-stock problem, that is, a product can only be sold one time in a C2C system. This poses several unique challenges for click-through rate (CTR) prediction. Due to limited user interactions for each product (i.e. item), the corresponding item embedding in the CTR model may not easily converge. This makes the conventional sequence modeling based approaches cannot…