Article

Transfer learning in collaborative filtering for sparsity reduction

Authors:

Qiang YangAuthors Info & Claims

AAAI'10: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence

Pages 230 - 235

Published: 11 July 2010 Publication History

Abstract

Data sparsity is a major problem for collaborative filtering (CF) techniques in recommender systems, especially for new users and items. We observe that, while our target data are sparse for CF systems, related and relatively dense auxiliary data may already exist in some other more mature application domains. In this paper, we address the data sparsity problem in a target domain by transferring knowledge about both users and items from auxiliary data sources. We observe that in different domains the user feedbacks are often heterogeneous such as ratings vs. clicks. Our solution is to integrate both user and item knowledge in auxiliary data sources through a principled matrix-based transfer learning framework that takes into account the data heterogeneity. In particular, we discover the principle coordinates of both users and items in the auxiliary data matrices, and transfer them to the target domain in order to reduce the effect of data sparsity. We describe our method, which is known as coordinate system transfer or CST, and demonstrate its effectiveness in alleviating the data sparsity problem in collaborative filtering. We show that our proposed method can significantly outperform several state-of-the-art solutions for this problem.

References

[1]

Bell, R. M., and Koren, Y. 2007. Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In ICDM, 43-52.

[2]

Buono, N. D., and Politi, T. 2004. A continuous technique for the weighted low-rank approximation problem. In ICCSA (2), 988-997.

[3]

Caruana, R. 1997. Multitask learning. Machine Learning 28(1):41-75.

[4]

Deerwester, S.; Dumais, S. T.; Furnas, G. W.; Landauer, T. K.; and Harshman, R. 1990. Indexing by latent semantic analysis. Journal of the American Society for Information Science 41:391-407.

[5]

Ding, C. H. Q.; Li, T.; Peng, W.; and Park, H. 2006. Orthogonal nonnegative matrix tri-factorizations for clustering. In KDD, 126-135.

[6]

Edelman, A.; Arias, T. A.; and Smith, S. T. 1999. The geometry of algorithms with orthogonality constraints. SIAM Journal on Matrix Analysis and Applications 20(2):303-353.

[7]

Hu, Y.; Koren, Y.; and Volinsky, C. 2008. Collaborative filtering for implicit feedback datasets. In ICDM, 263-272.

[8]

Keshavan, R.; Montanari, A.; and Oh, S. 2010. Matrix completion from noisy entries. In NIPS, 952-960.

[9]

Kienzle, W., and Chellapilla, K. 2006. Personalized handwriting recognition via biased regularization. In ICML, 457-464.

[10]

Li, B.; Yang, Q.; and Xue, X. 2009a. Can movies and books collaborate? cross-domain collaborative filtering for sparsity reduction. In IJCAI, 2052-2057.

[11]

Li, B.; Yang, Q.; and Xue, X. 2009b. Transfer learning for collaborative filtering via a rating-matrix generative model. In ICML, 617-624.

[12]

Long, B.; Zhang, Z. M.; Wu, X.; and Yu, P. S. 2007. Relational clustering by symmetric convex coding. In ICML, 569-576.

[13]

Nathanson, T.; Bitton, E.; and Goldberg, K. 2007. Eigen-taste 5.0: constant-time adaptability in a recommender system using item clustering. In RecSys, 149-152.

[14]

Pan, S. J., and Yang, Q. 2009. A survey on transfer learning. IEEE TKDE. IEEE Computer Society, http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.191.

[15]

Resnick, P.; Iacovou, N.; Suchak, M.; Bergstrom, P.; and Riedl, J. 1994. Grouplens: An open architecture for collaborative filtering of netnews. In CSCW, 175-186.

[16]

Si, L., and Jin, R. 2003. Flexible mixture model for collaborative filtering. In ICML, 704-711.

[17]

Sindhwani, V.; Bucak, S.; Hu, J.; and Mojsilovic, A. 2009. A family of non-negative matrix factorizations for one-class collaborative filtering. In RecSys '09: Recommender based Industrial Applications Workshop.

[18]

Singh, A. P., and Gordon, G. J. 2008. Relational learning via collective matrix factorization. In KDD, 650-658.

Cited By

Samra AFrolov EVasilev AGrigorevskiy AVakhrushev A(2024)Cross-Domain Latent Factors Sharing via Implicit Matrix FactorizationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688143(309-317)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688143
Tran TFelfernig ALe VChau TMai T(2023)User Needs for Explanations of Recommendations: In-depth Analyses of the Role of Item Domain and Personal CharacteristicsProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3592950(54-65)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3565472.3592950
Zhu YTang ZLiu YZhuang FXie RZhang XLin LHe QSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)Personalized Transfer of User Preferences for Cross-domain RecommendationProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498392(1507-1515)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498392
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AAAI'10: Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence

July 2010

1970 pages

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AAAI Press

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Published: 11 July 2010

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Samra AFrolov EVasilev AGrigorevskiy AVakhrushev A(2024)Cross-Domain Latent Factors Sharing via Implicit Matrix FactorizationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688143(309-317)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688143
Tran TFelfernig ALe VChau TMai T(2023)User Needs for Explanations of Recommendations: In-depth Analyses of the Role of Item Domain and Personal CharacteristicsProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3592950(54-65)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3565472.3592950
Zhu YTang ZLiu YZhuang FXie RZhang XLin LHe QSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)Personalized Transfer of User Preferences for Cross-domain RecommendationProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498392(1507-1515)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498392
Ma MRen PChen ZRen ZZhao LLiu PMa Jde Rijke M(2022)Mixed Information Flow for Cross-Domain Sequential RecommendationsACM Transactions on Knowledge Discovery from Data10.1145/348733116:4(1-32)Online publication date: 8-Jan-2022
https://dl.acm.org/doi/10.1145/3487331
Niu SJiang YChen BWang JLiu YSong H(2021)Cross-Modality Transfer Learning for Image-Text Information ManagementACM Transactions on Management Information Systems10.1145/346432413:1(1-14)Online publication date: 5-Oct-2021
https://dl.acm.org/doi/10.1145/3464324
Gao CLi YFeng FChen XZhao KHe XJin D(2021)Cross-domain Recommendation with Bridge-Item EmbeddingsACM Transactions on Knowledge Discovery from Data10.1145/344768316:1(1-23)Online publication date: 20-Jul-2021
https://dl.acm.org/doi/10.1145/3447683
Zhu YGe KZhuang FXie RXi DZhang XLin LHe QDiaz FShah CSuel TCastells PJones RSakai T(2021)Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start UsersProceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3404835.3463010(1813-1817)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3404835.3463010
Krishnan ADas MBendre MYang HSundaram HHuang JChang YCheng XKamps JMurdock VWen JLiu Y(2020)Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain RecommendationProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401078(1081-1090)Online publication date: 25-Jul-2020
https://dl.acm.org/doi/10.1145/3397271.3401078
Yu WLin XGe JOu WQin ZGupta RLiu YShah MRajan STang JPrakash B(2020)Semi-supervised Collaborative Filtering by Text-enhanced Domain AdaptationProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403264(2136-2144)Online publication date: 23-Aug-2020
https://dl.acm.org/doi/10.1145/3394486.3403264
Zhou XLiu DLian JXie X(2019)Collaborative metric learning with memory network for multi-relational recommender systemsProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367471.3367661(4454-4460)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367471.3367661
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