# SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback

arXiv: https://arxiv.org/abs/2002.09841 · HTML: https://mlsanity.com/paper/2002.09841/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2020.

## Built on by (most cited first, 15 of 15)

- [DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation](https://mlsanity.com/paper/2206.10848/) (2022)
- [Cross Pairwise Ranking for Unbiased Item Recommendation](https://mlsanity.com/paper/2204.12176/) (2022)
- [Determinantal Point Process Likelihoods for Sequential Recommendation](https://mlsanity.com/paper/2204.11562/) (2022)
- [Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation](https://mlsanity.com/paper/2402.02855/) (2024)
- [Enhancing Question Answering for Enterprise Knowledge Bases using Large Language Models](https://mlsanity.com/paper/2404.08695/) (2024)
- [Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation](https://mlsanity.com/paper/2105.07377/) (2021)
- [DISCO: A Hierarchical Disentangled Cognitive Diagnosis Framework for Interpretable Job Recommendation](https://mlsanity.com/paper/2410.07671/) (2024)
- [Hi-SAM: A Hierarchical Structure-Aware Multi-modal Framework for Large-Scale Recommendation](https://mlsanity.com/paper/2602.11799/) (2026)
- [KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?](https://mlsanity.com/paper/2404.03164/) (2024)
- [Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning](https://mlsanity.com/paper/2407.00082/) (2024)
- [Rethinking Popularity Bias in Collaborative Filtering via Analytical Vector Decomposition](https://mlsanity.com/paper/2512.10688/) (2025)
- [MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation](https://mlsanity.com/paper/2603.25126/) (2026)
- [URIR: Recommendation algorithm of user RNN encoder and item encoder based on knowledge graph](https://mlsanity.com/paper/2111.00739/) (2021)
- [Sharpness-Aware Poisoning: Enhancing Transferability of Injective Attacks on Recommender Systems](https://mlsanity.com/paper/2604.22170/) (2026)
- [Against Political Polarization: A Unified Framework for Tracing Evolving Political Ideologies on Social Media](https://mlsanity.com/paper/2608.17987/) (2026)

## Abstract (arXiv)

The recent development of online recommender systems has a focus on collaborative ranking from implicit feedback, such as user clicks and purchases. Different from explicit ratings, which reflect graded user preferences, the implicit feedback only generates positive and unobserved labels. While considerable efforts have been made in this direction, the well-known pairwise and listwise approaches have still been limited by various challenges. Specifically, for the pairwise approaches, the assumption of independent pairwise preference is not always held in practice. Also, the listwise approaches cannot efficiently accommodate "ties" due to the precondition of the entire list permutation. To this end, in this paper, we propose a novel setwise Bayesian approach for collaborative ranking, namely SetRank, to inherently accommodate the characteristics of implicit feedback in recommender system. Specifically, SetRank aims at maximizing the posterior probability of novel setwise preference comparisons and can be implemented with matrix factorization and neural networks. Meanwhile, we also present the theoretical analysis of SetRank to show that the bound of excess risk can be proportional to √M/N, where M and N are the numbers of items and users, respectively. Finally, extensive experiments on four real-world datasets clearly validate the superiority of SetRank compared with various state-of-the-art baselines.