COMET: Convolutional Dimension Interaction for Deep Matrix Factorization release_yvpnopkekvce7oh7kddl7vkeyi

by Zhuoyi Lin, Lei Feng, Xingzhi Guo, Rui Yin, Chee Keong Kwoh, Chi Xu

Released as a article .

2020  

Abstract

Latent factor models play a dominant role among recommendation techniques. However, most of the existing latent factor models assume embedding dimensions are independent of each other, and thus regrettably ignore the interaction information across different embedding dimensions. In this paper, we propose a novel latent factor model called COMET (COnvolutional diMEnsion inTeraction), which provides the first attempt to model higher-order interaction signals among all latent dimensions in an explicit manner. To be specific, COMET stacks the embeddings of historical interactions horizontally, which results in two "embedding maps" that encode the original dimension information. In this way, users' and items' internal interactions can be exploited by convolutional neural networks with kernels of different sizes and a fully-connected multi-layer perceptron. Furthermore, the representations of users and items are enriched by the learnt interaction vectors, which can further be used to produce the final prediction. Extensive experiments and ablation studies on various public implicit feedback datasets clearly demonstrate the effectiveness and the rationality of our proposed method.
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Date   2020-07-28
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arXiv  2007.14129v1
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