COMET: Convolutional Dimension Interaction for Deep Matrix Factorization
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Zhuoyi Lin, Lei Feng, Xingzhi Guo, Rui Yin, Chee Keong Kwoh, Chi Xu
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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