COMET: Convolutional Dimension Interaction for Collaborative Filtering

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COMET: Convolutional Dimension Interaction for Collaborative Filtering
Title:
COMET: Convolutional Dimension Interaction for Collaborative Filtering
Journal Title:
ACM Transactions on Intelligent Systems and Technology
Publication Date:
23 March 2023
Citation:
Lin, Z., Feng, L., Guo, X., Zhang, Y., Yin, R., Kwoh, C. K., & Xu, C. (2023). COMET: Convolutional Dimension Interaction for Collaborative Filtering. ACM Transactions on Intelligent Systems and Technology, 14(4), 1–18. https://doi.org/10.1145/3588576
Abstract:
Representation learning-based recommendation models play a dominant role among recommendation techniques. However, most of the existing methods 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 article, we propose a novel representation learning-based model called COMET ( CO nvolutional di M E nsion in T eraction), which simultaneously models 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 (CNN) with kernels of different sizes simultaneously. A fully connected multi-layer perceptron (MLP) 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 rationality of our proposed method.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR / Nanyang Technological University (NTU)/Singapore University of Technology and Design (SUTD) - AI Partnership
Grant Reference no. : RGANS1905

This research / project is supported by the Singapore Institute of Manufacturing Technology-Nanyang Technological University (SIMTech-NTU) - Joint Laboratory and Collaborative Research Programme on Complex Systems
Grant Reference no. : N.A
Description:
© Author | ACM 2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Intelligent Systems and Technology, http://dx.doi.org/10.1145/3588576
ISSN:
2157-6904
2157-6912
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