Unraveling Metric Vector Spaces with Factorization for Recommendation

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Authors Shuai Zhang, Lina Yao, B. Wu, Xiwei Xu, X. Zhang, Liming Zhu
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Paper Abstract Unlike all prior work, in this article, we investigate the notion of “unraveling metric vector spaces,” i.e., deriving meaning and low-rank structure from distance or metric space. Our new model bridges two commonly adopted paradigms for recommendations-metric learning approaches and factorization-based models, distinguishing itself accordingly. More concretely, we show that factorizing a metric vector space can be surprisingly efficacious. All in all, our proposed method, factorized metric learning, is highly effective for two classic recommendation tasks, possessing the potential of displacing many popular choices as an extremely strong baseline. We have done experiments on a number of real-world datasets, which show that our model performs better than recent state of the art largely on the rating prediction and item ranking tasks.
Date of publication 2020
Code Programming Language Python
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