COMET: Convolutional Dimension Interaction for Collaborative Filtering
- URL: http://arxiv.org/abs/2007.14129v5
- Date: Tue, 17 Aug 2021 18:18:53 GMT
- Title: COMET: Convolutional Dimension Interaction for Collaborative Filtering
- Authors: Zhuoyi Lin, Lei Feng, Xingzhi Guo, Yu Zhang, Rui Yin, Chee Keong Kwoh,
Chi Xu
- Abstract summary: We propose a novel latent factor model called COMET, which simultaneously model the high-order interaction patterns among historical interactions and embedding dimensions.
To be specific, COMET stacks the embeddings of historical interactions horizontally at first, which results in two "embedding maps"
In this way, internal interactions and dimensional interactions can be exploited by convolutional neural networks with kernels of different sizes simultaneously.
- Score: 16.799611667681233
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Latent factor models play a dominant role among recommendation techniques.
However, most of the existing latent factor models assume both historical
interactions and embedding dimensions are independent of each other, and thus
regrettably ignore the high-order interaction information among historical
interactions and embedding dimensions. In this paper, we propose a novel latent
factor model called COMET (COnvolutional diMEnsion inTeraction), which
simultaneously model the high-order interaction patterns among historical
interactions and embedding dimensions. To be specific, COMET stacks the
embeddings of historical interactions horizontally at first, which results in
two "embedding maps". In this way, internal interactions and dimensional
interactions can be exploited by convolutional neural networks with kernels of
different sizes simultaneously. A fully-connected multi-layer perceptron is
then applied to obtain two interaction vectors. Lastly, 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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