Regularizing Matrix Factorization with User and Item Embeddings for Recommendation

Thanh Tran, Kyumin Lee, Yiming Liao, Dongwon Lee
8/31/2018
cs.IRcs.AI

Official code: thanhdtran/RME (46 stars, last commit 2019-04-17).

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.

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thanhdtran/RMEOfficial100%

Regularizing Matrix Factorization with User and Item Embeddings for Recommendation -- CIKM 2018

4621PythonAug 15, 20187 years ago

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Cite this paper

@article{tran2018regularizing,
  title  = {Regularizing Matrix Factorization with User and Item Embeddings for Recommendation},
  author = {Thanh Tran and Kyumin Lee and Yiming Liao and Dongwon Lee},
  year   = {2018},
  eprint = {1809.00979},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1809.00979}
}

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