The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Fast Matrix Factorization for Online Recommendation with Implicit Feedback
Open accessAbstract
This paper contributes improvements on both the effectiveness and efficiency of Matrix Factorization (MF) methods for implicit feedback. We highlight two critical issues of existing works. First, due to the large space of unobserved feedback, most existing works resort to assign a uniform weight to the missing data to reduce computational complexity. However, such a uniform assumption is invalid in real-world settings. Second, most methods are also designed in an offline setting and fail to keep up with the dynamic nature of online data. We address the above two issues in learning MF models from implicit feedback. We first propose to weight the missing data based on item popularity, which is more effective and flexible than the uniform-weight assumption. However, such a non-uniform weighting poses efficiency challenge in learning the model. To address this, we specifically design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) technique, for efficiently optimizing a MF model with variably-weighted missing data. We exploit this efficiency to then seamlessly devise an incremental update strategy that instantly refreshes a MF model given new feedback. Through comprehensive experiments on two public datasets in both offline and online protocols, we show that our implemented, open-source (https://github.com/hexiangnan/sigir16-eals) eALS consistently outperforms state-of-the-art implicit MF methods.
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Cites
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BPR: Bayesian Personalized Ranking from Implicit Feedback
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Collaborative Filtering for Implicit Feedback Datasets
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Neural Word Embedding as Implicit Matrix Factorization
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Cited by
Xiao, J., Ye, H., He, X., Zhang, H., Wu, F., & Chua, T.-S. (2017) · Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/435 · Cited by 912
Self-citationChen, J., Zhang, H., He, X., Nie, L., Liu, W., & Chua, T.-S. (2017) · Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3077136.3080797 · Cited by 851
Self-citationBias and Debias in Recommender System: A Survey and Future Directions
Chen, J., Dong, H., Wang, X., Feng, F., Wang, M., & He, X. (2023) · ACM Transactions on Information Systems · doi:10.1145/3564284 · Cited by 741
Self-citationHierarchical Gating Networks for Sequential Recommendation
Ma, C., Kang, P., & Liu, X. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330984 · Cited by 322
Lei, W., He, X., Miao, Y., Wu, Q., Hong, R., Kan, M.-Y., & Chua, T.-S. (2020) · Proceedings of the 13th International Conference on Web Search and Data Mining · doi:10.1145/3336191.3371769 · Cited by 253
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Metadata
- Authors
- He, X., Zhang, H., Kan, M.-Y., & Chua, T.-S.
- Venue
- Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2016-07-07
- Pages
- 549-558
- OpenAlex
- W2340502990
- Cited by
- 988
- References
- 32
- FWCI
- 94.59
- Type
- conference paper
- Language
- en
- Open access
- green
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