Volume 1, Issue 1March 2023Current Issue
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research-article
User Cold-start Problem in Multi-armed Bandits: When the First Recommendations Guide the User’s Experience
Nowadays, Recommender Systems have played a crucial role in several entertainment scenarios by making personalised recommendations and guiding the entire users’ journey from their first interaction. Recent works have addressed it as a Contextual Bandit by ...
survey
A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
- Chen Gao,
- Yu Zheng,
- Nian Li,
- Yinfeng Li,
- Yingrong Qin,
- Jinghua Piao,
- Yuhan Quan,
- Jianxin Chang,
- Depeng Jin,
- Xiangnan He,
- Yong Li
Recommender system is one of the most important information services on today’s Internet. Recently, graph neural networks have become the new state-of-the-art approach to recommender systems. In this survey, we conduct a comprehensive review of the ...
SECTION: Highlights of ACM RecSys '21
research-article
Pessimistic Decision-Making for Recommender Systems
Modern recommender systems are often modelled under the sequential decision-making paradigm, where the system decides which recommendations to show in order to maximise some notion of either imminent or long-term reward. Such methods often require an ...
research-article
Debiased Representation Learning in Recommendation via Information Bottleneck
How to effectively mitigate the bias of feedback in recommender systems is an important research topic. In this article, we first describe the generation process of the biased and unbiased feedback in recommender systems via two respective causal diagrams,...
research-article
Auditing YouTube’s Recommendation Algorithm for Misinformation Filter Bubbles
- Ivan Srba,
- Robert Moro,
- Matus Tomlein,
- Branislav Pecher,
- Jakub Simko,
- Elena Stefancova,
- Michal Kompan,
- Andrea Hrckova,
- Juraj Podrouzek,
- Adrian Gavornik,
- Maria Bielikova
In this article, we present results of an auditing study performed over YouTube aimed at investigating how fast a user can get into a misinformation filter bubble, but also what it takes to “burst the bubble,” i.e., revert the bubble enclosure. We employ ...