Abstract
Matrix factorization is a widely used collaborative filtering technique. However, the inner-product it relies on is not a proper distance metric because it does not satisfy the triangle inequality. Therefore, it cannot reliably capture similarities of neither item-item pairs nor user-user pairs, which will lead to suboptimal performance and limited interpretability. To solve these problems, we propose a novel collaborative filtering method based on metric learning, which can simultaneously capture the similarities of item-item pairs and user-user pairs besides the users’ preferences on items. Different from previous metric learning methods which always only use either global structure information or local neighborhood information, the proposed method integrates both of these two kinds of information within a probability framework. Experimental results confirm the effectiveness of the proposed method.
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Acknowledgements
This work was sponsored by National Key R&D Program of China (Grant No. 2017YFB1002002).
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Liu, H., Du, Y., Wu, Z. (2018). Collaborative Probability Metric Learning. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10987. Springer, Cham. https://doi.org/10.1007/978-3-319-96890-2_17
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DOI: https://doi.org/10.1007/978-3-319-96890-2_17
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