Introduction to the Special Issue on Trustworthy Recommender Systems
This editorial introduces the Special Issue on Trustworthy Recommender Systems, hosted by the ACM Transactions on Recommender Systems in 2024. We provide an overview on the multifaceted aspects of trustworthiness and point to recent regulations that ...
A Survey on Trustworthy Recommender Systems
- Yingqiang Ge,
- Shuchang Liu,
- Zuohui Fu,
- Juntao Tan,
- Zelong Li,
- Shuyuan Xu,
- Yunqi Li,
- Yikun Xian,
- Yongfeng Zhang
Recommender systems (RS), serving at the forefront of Human-centered AI, are widely deployed in almost every corner of the web and facilitate the human decision-making process. However, despite their enormous capabilities and potential, RS may also lead ...
A Privacy Preserving System for Movie Recommendations Using Federated Learning
Recommender systems have become ubiquitous in the past years. They solve the tyranny of choice problem faced by many users, and are utilized by many online businesses to drive engagement and sales. Besides other criticisms, like creating filter bubbles ...
Evaluating Impact of User-Cluster Targeted Attacks in Matrix Factorisation Recommenders
In practice, users of a Recommender System (RS) fall into a few clusters based on their preferences. In this work, we conduct a systematic study on user-cluster targeted data poisoning attacks on Matrix Factorisation (MF)-based RS, where an adversary ...
Explainable Meta-Path Based Recommender Systems
Meta-paths have been popularly used to provide explainability in recommendations. Although long/complicated meta-paths could represent complex user-item connectivity, they are not easy to interpret. This work tackles this problem by introducing a meta-...
Topic-Centric Explanations for News Recommendation
News recommender systems (NRS) have been widely applied for online news websites to help users find relevant articles based on their interests. Recent methods have demonstrated considerable success in terms of recommendation performance. However, the lack ...
Evaluation Measures of Individual Item Fairness for Recommender Systems: A Critical Study
Fairness is an emerging and challenging topic in recommender systems. In recent years, various ways of evaluating and therefore improving fairness have emerged. In this study, we examine existing evaluation measures of fairness in recommender systems. ...
Mitigating Exposure Bias in Recommender Systems—A Comparative Analysis of Discrete Choice Models
When implicit feedback recommender systems expose users to items, they influence the users’ choices and, consequently, their own future recommendations. This effect is known as exposure bias, and it can cause undesired effects such as filter bubbles and ...
Fairness of Interaction in Ranking under Position, Selection, and Trust Bias
Ranking algorithms in online platforms serve not only users on the demand side, but also items on the supply side. While ranking has traditionally presented items in an order that maximizes their utility to users, the uneven interactions that different ...
Dynamic Fairness-aware Recommendation Through Multi-agent Social Choice
- Amanda Aird,
- Paresha Farastu,
- Joshua Sun,
- Elena Stefancová,
- Cassidy All,
- Amy Voida,
- Nicholas Mattei,
- Robin Burke
Algorithmic fairness in the context of personalized recommendation presents significantly different challenges to those commonly encountered in classification tasks. Researchers studying classification have generally considered fairness to be a matter of ...
Recommendation Unlearning via Influence Function
Recommendation unlearning is an emerging task to serve users for erasing unusable data (e.g., some historical behaviors) from a well-trained recommender model. Existing methods process unlearning requests by fully or partially retraining the model after ...
A Survey on Intent-aware Recommender Systems
Many modern online services feature personalized recommendations. A central challenge when providing such recommendations is that the reason why an individual user accesses the service may change from visit to visit or even during an ongoing usage ...
