# Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback

arXiv: https://arxiv.org/abs/2003.01892 · HTML: https://mlsanity.com/paper/2003.01892/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2020.

## Built on by (most cited first, 17 of 17)

- [AutoDebias: Learning to Debias for Recommendation](https://mlsanity.com/paper/2105.04170/) (2021)
- [A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation](https://mlsanity.com/paper/2104.13030/) (2021)
- [DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation](https://mlsanity.com/paper/2206.10848/) (2022)
- [Denoising User-aware Memory Network for Recommendation](https://mlsanity.com/paper/2107.05474/) (2021)
- [Debiased Explainable Pairwise Ranking from Implicit Feedback](https://mlsanity.com/paper/2107.14768/) (2021)
- [Rethinking Missing Data: Aleatoric Uncertainty-Aware Recommendation](https://mlsanity.com/paper/2209.11679/) (2022)
- [BASM: A Bottom-up Adaptive Spatiotemporal Model for Online Food Ordering Service](https://mlsanity.com/paper/2211.12033/) (2022)
- [Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers](https://mlsanity.com/paper/2301.09210/) (2023)
- [USM: Unbiased Survey Modeling for Limiting Negative User Experiences in Recommendation Systems](https://mlsanity.com/paper/2412.10674/) (2024)
- [Entropy Minimizing Matrix Factorization](https://mlsanity.com/paper/2103.13487/) (2021)
- [CDR: Conservative Doubly Robust Learning for Debiased Recommendation](https://mlsanity.com/paper/2308.08461/) (2023)
- [Towards Reliable Negative Sampling for Recommendation with Implicit Feedback via In-Community Popularity](https://mlsanity.com/paper/2602.18759/) (2026)
- [Scalable Personalised Item Ranking through Parametric Density Estimation](https://mlsanity.com/paper/2105.04769/) (2021)
- [Graph-incorporated Latent Factor Analysis for High-dimensional and Sparse Matrices](https://mlsanity.com/paper/2204.07818/) (2022)
- [Generating Self-Serendipity Preference in Recommender Systems for Addressing Cold Start Problems](https://mlsanity.com/paper/2204.12651/) (2022)
- [Cross-Domain Latent Factors Sharing via Implicit Matrix Factorization](https://mlsanity.com/paper/2409.15568/) (2024)
- [A Scenario-Oriented Survey of Federated Recommender Systems: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2508.19620/) (2025)

## Abstract (arXiv)

Recommendation from implicit feedback is a highly challenging task due to the lack of the reliable observed negative data. A popular and effective approach for implicit recommendation is to treat unobserved data as negative but downweight their confidence. Naturally, how to assign confidence weights and how to handle the large number of the unobserved data are two key problems for implicit recommendation models. However, existing methods either pursuit fast learning by manually assigning simple confidence weights, which lacks flexibility and may create empirical bias in evaluating user's preference; or adaptively infer personalized confidence weights but suffer from low efficiency. To achieve both adaptive weights assignment and efficient model learning, we propose a fast adaptively weighted matrix factorization (FAWMF) based on variational auto-encoder. The personalized data confidence weights are adaptively assigned with a parameterized neural network (function) and the network can be inferred from the data. Further, to support fast and stable learning of FAWMF, a new specific batch-based learning algorithm fBGD has been developed, which trains on all feedback data but its complexity is linear to the number of observed data. Extensive experiments on real-world datasets demonstrate the superiority of the proposed FAWMF and its learning algorithm fBGD.