Abstract
We have developed a framework for jointly conducting collaborative filtering and distance metric learning based on regularized singular value decomposition (RSVD), which discovers the user matrix and item matrix in the low rank space. Our approach is able to solve RSVD and simultaneously learn the parameters of Mahalanobis distance considering the ratings given by similar users and dissimilar users. One characteristic of our approach is that the learned model can be effectively applied to rating prediction and other relevant applications such as trust prediction, resulting in a solution which is coherent and optimal to both tasks. Another characteristic is that social community information and similarity information can be easily considered in our framework. We have conducted extensive experiments on rating prediction using real-world datasets to evaluate our framework. We have also compared our framework with other existing works to illustrate the effectiveness. Experimental results show that our framework achieves a promising prediction performance and outperforms the existing works.
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Notes
- 1.
- 2.
In CF, sometimes we directly solve \(R \approx U' V\) in which \(\varSigma \) is embedded in U and V.
- 3.
The dataset can be freely downloaded in http://www.grouplens.org/.
- 4.
The dataset can be freely downloaded in http://www.trustlet.org/wiki/Downloaded_Epinions_dataset.
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Acknowledgments
The work described in this paper is substantially supported by grants from the Education University of Hong Kong (Project Codes: RG 30/2014-2015R and RG 18/2015-2016R).
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Wong, TL., Lam, W., Xie, H., Wang, F.L. (2016). A Joint Framework for Collaborative Filtering and Metric Learning. In: Ma, S., et al. Information Retrieval Technology. AIRS 2016. Lecture Notes in Computer Science(), vol 9994. Springer, Cham. https://doi.org/10.1007/978-3-319-48051-0_14
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