Overhead-free User-side Recommender Systems
Not on the map. Why?
Abstract (arXiv)
Traditionally, recommendation algorithms have been designed for service developers. But recently, a new paradigm called user-side recommender systems has been proposed. User-side recommender systems are built and used by end users, in sharp contrast to traditional provider-side recommender systems. Even if the official recommender system offered by the provider is not fair, end users can create and enjoy their own user-side recommender systems by themselves. Although the concept of user-side recommender systems is attractive, the problem is they require tremendous communication costs between the user and the official system. Even the most efficient user-side recommender systems require about 5 times more costs than provider-side recommender systems. Such high costs hinder the adoption of user-side recommender systems. In this paper, we propose overhead-free user-side recommender systems, RecCycle, which realizes user-side recommender systems without any communication overhead. The main idea of RecCycle is to recycle past recommendation results offered by the provider's recommender systems. The ingredients of RecCycle can be retrieved ``for free,'' and it greatly reduces the cost of user-side recommendations. In the experiments, we confirm that RecCycle performs as well as state-of-the-art user-side recommendation algorithms while RecCycle reduces costs significantly.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 24 of its 51 references that are in the map. First posted on arXiv 12 Nov 2024.
Builds on
24 of 51 references.
References · 24
- 2016Equality of Opportunity in Supervised Learningcited by 2,000
- 2017Neural Collaborative Filteringcited by 1,108
- 2015Image-based Recommendations on Styles and Substitutescited by 702
- 2016Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filteringcited by 517
- 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
- 2018Fairness of Exposure in Rankingscited by 189
- 2019Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Searchcited by 137
- 2019Fairness in Recommendation Ranking through Pairwise Comparisonscited by 127
- 2018Equity of Attention: Amortizing Individual Fairness in Rankingscited by 124
- 2017FA*IR: A Fair Top-k Ranking Algorithmcited by 113
- 2017Beyond Parity: Fairness Objectives for Collaborative Filteringcited by 93
- 2020Fairness-Aware Explainable Recommendation over Knowledge Graphscited by 74
12 more
- 2017Multisided Fairness for Recommendationcited by 69
- 2020Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approachcited by 32
- 2018Exploring Author Gender in Book Rating and Recommendationcited by 29
- 2023Embarrassingly Simple Text Watermarkscited by 23
- 2019Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfactioncited by 14
- 2020Algorithmic Decision Making with Conditional Fairnesscited by 14
- 2021Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data?cited by 7
- 2022Towards Principled User-side Recommender Systemscited by 7
- 2021Retrieving Black-box Optimal Images from External Databasescited by 6
- 2022CLEAR: A Fully User-side Image Search Systemcited by 6
- 2023Making Translators Privacy-aware on the User's Sidecited by 3
- 2024User-Side Realizationcited by 3
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.