# Item Recommendation from Implicit Feedback

arXiv: https://arxiv.org/abs/2101.08769 · HTML: https://mlsanity.com/paper/2101.08769/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 21 Jan 2021.

## Builds on (most cited first, 10 of 10)

- [Neural Collaborative Filtering vs. Matrix Factorization Revisited](https://mlsanity.com/paper/2005.09683/) (2020)
- [A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research](https://mlsanity.com/paper/1911.07698/) (2019)
- [On the Difficulty of Evaluating Baselines: A Study on Recommender Systems](https://mlsanity.com/paper/1905.01395/) (2019)
- [Learning to Hash for Indexing Big Data - A Survey](https://mlsanity.com/paper/1509.05472/) (2015)
- [Adaptive Sampled Softmax with Kernel Based Sampling](https://mlsanity.com/paper/1712.00527/) (2017)
- [Sampled Softmax with Random Fourier Features](https://mlsanity.com/paper/1907.10747/) (2019)
- [Beyond Greedy Ranking: Slate Optimization via List-CVAE](https://mlsanity.com/paper/1803.01682/) (2018)
- [A Generic Coordinate Descent Framework for Learning from Implicit Feedback](https://mlsanity.com/paper/1611.04666/) (2016)
- [Efficient Training on Very Large Corpora via Gramian Estimation](https://mlsanity.com/paper/1807.07187/) (2018)
- [TAPAS: Two-pass Approximate Adaptive Sampling for Softmax](https://mlsanity.com/paper/1707.03073/) (2017)

## Built on by (most cited first, 18 of 18)

- [Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion](https://mlsanity.com/paper/2310.20453/) (2023)
- [On Softmax Direct Preference Optimization for Recommendation](https://mlsanity.com/paper/2406.09215/) (2024)
- [gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling](https://mlsanity.com/paper/2308.07192/) (2023)
- [Revisiting the Performance of iALS on Item Recommendation Benchmarks](https://mlsanity.com/paper/2110.14037/) (2021)
- [How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective](https://mlsanity.com/paper/2404.12008/) (2024)
- [Language Representations Can be What Recommenders Need: Findings and Potentials](https://mlsanity.com/paper/2407.05441/) (2024)
- [Robust Collaborative Filtering to Popularity Distribution Shift](https://mlsanity.com/paper/2310.10696/) (2023)
- [Revisiting Neighborhood-based Link Prediction for Collaborative Filtering](https://mlsanity.com/paper/2203.15789/) (2022)
- [Preference Diffusion for Recommendation](https://mlsanity.com/paper/2410.13117/) (2024)
- [Revisiting Recommendation Loss Functions through Contrastive Learning (Technical Report)](https://mlsanity.com/paper/2312.08520/) (2023)
- [BSL: Understanding and Improving Softmax Loss for Recommendation](https://mlsanity.com/paper/2312.12882/) (2023)
- [iALS++: Speeding up Matrix Factorization with Subspace Optimization](https://mlsanity.com/paper/2110.14044/) (2021)
- [Bilateral Self-unbiased Learning from Biased Implicit Feedback](https://mlsanity.com/paper/2207.12660/) (2022)
- [Extracting Interaction-Aware Monosemantic Concepts in Recommender Systems](https://mlsanity.com/paper/2511.18024/) (2025)
- [UIPC-MF: User-Item Prototype Connection Matrix Factorization for Explainable Collaborative Filtering](https://mlsanity.com/paper/2308.07048/) (2023)
- [(Debiased) Contrastive Learning Loss for Recommendation (Technical Report)](https://mlsanity.com/paper/2312.08517/) (2023)
- [NDCG-Consistent Softmax Approximation with Accelerated Convergence](https://mlsanity.com/paper/2506.09454/) (2025)
- [Evidence Before Sampling: Interpretable Implicit Negative Candidate Discovery for Recommendation](https://mlsanity.com/paper/2610.07708/) (2026)

## Abstract (arXiv)

The task of item recommendation is to select the best items for a user from a large catalogue of items. Item recommenders are commonly trained from implicit feedback which consists of past actions that are positive only. Core challenges of item recommendation are (1) how to formulate a training objective from implicit feedback and (2) how to efficiently train models over a large item catalogue. This article provides an overview of item recommendation, its unique characteristics and some common approaches. It starts with an introduction to the problem and discusses different training objectives. The main body deals with learning algorithms and presents sampling based algorithms for general recommenders and more efficient algorithms for dot product models. Finally, the application of item recommenders for retrieval tasks is discussed.