# Regularizing Matrix Factorization with User and Item Embeddings for Recommendation

arXiv: https://arxiv.org/abs/1809.00979 · HTML: https://mlsanity.com/paper/1809.00979/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2018.

## Built on by (most cited first, 6 of 6)

- [DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation](https://mlsanity.com/paper/2206.10848/) (2022)
- [Knowledge-Enhanced Top-K Recommendation in Poincar'e Ball](https://mlsanity.com/paper/2101.04852/) (2021)
- [Nonparametric Embeddings of Sparse High-Order Interaction Events](https://mlsanity.com/paper/2207.03639/) (2022)
- [Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation](https://mlsanity.com/paper/2208.01709/) (2022)
- [Enhancing E-Commerce Recommendation using Pre-Trained Language Model and Fine-Tuning](https://mlsanity.com/paper/2302.04443/) (2023)
- [SUPER-Rec: SUrrounding Position-Enhanced Representation for Recommendation](https://mlsanity.com/paper/2209.04154/) (2022)

## Abstract (arXiv)

Following recent successes in exploiting both latent factor and word embedding models in recommendation, we propose a novel Regularized Multi-Embedding (RME) based recommendation model that simultaneously encapsulates the following ideas via decomposition: (1) which items a user likes, (2) which two users co-like the same items, (3) which two items users often co-liked, and (4) which two items users often co-disliked. In experimental validation, the RME outperforms competing state-of-the-art models in both explicit and implicit feedback datasets, significantly improving Recall@5 by 5.9~7.0%, NDCG@20 by 4.3~5.6%, and MAP@10 by 7.9~8.9%. In addition, under the cold-start scenario for users with the lowest number of interactions, against the competing models, the RME outperforms NDCG@5 by 20.2% and 29.4% in MovieLens-10M and MovieLens-20M datasets, respectively. Our datasets and source code are available at: https://github.com/thanhdtran/RME.git.