Regularizing Matrix Factorization with User and Item Embeddings for Recommendation

TL;DR

This paper introduces a novel recommendation model called Regularized Multi-Embedding (RME) that combines latent factors and embeddings to better capture user-item interactions, co-liking, and disliking patterns, leading to improved recommendation accuracy especially in cold-start scenarios.

Contribution

The paper proposes the RME model that integrates multiple embedding-based decomposition ideas for enhanced recommendation performance over state-of-the-art models.

Findings

RME outperforms competing models in explicit and implicit feedback datasets.

RME significantly improves metrics like Recall@5, NDCG@20, and MAP@10.

RME shows strong cold-start performance, especially for users with few interactions.

Abstract

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.