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Technol."],"published-print":{"date-parts":[[2023,8,31]]},"abstract":"<jats:p>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 (<jats:underline>CO<\/jats:underline>nvolutional di<jats:underline>M<\/jats:underline><jats:underline>E<\/jats:underline>nsion in<jats:underline>T<\/jats:underline>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 \u201cembedding maps\u201d. 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.<\/jats:p>","DOI":"10.1145\/3588576","type":"journal-article","created":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T12:21:13Z","timestamp":1679574073000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["COMET: Convolutional Dimension Interaction for Collaborative Filtering"],"prefix":"10.1145","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4942-160X","authenticated-orcid":false,"given":"Zhuoyi","family":"Lin","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, and Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2839-5799","authenticated-orcid":false,"given":"Lei","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3534-3877","authenticated-orcid":false,"given":"Xingzhi","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Stony Brook University, Stony Brook, NY, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4952-8095","authenticated-orcid":false,"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Institute of Cancer Research, London, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1403-0396","authenticated-orcid":false,"given":"Rui","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Health Outcomes and Biomedical Informatics,University of Florida, Gainesville, FL, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8547-6387","authenticated-orcid":false,"given":"Chee Keong","family":"Kwoh","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5480-3974","authenticated-orcid":false,"given":"Chi","family":"Xu","sequence":"additional","affiliation":[{"name":"Singapore Institute of Manufacturing Technology (SIMTech), Agency for Science, Technology and Research (A*STAR); and School of Computer Science and Engineering, Nanyang Technological University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,5,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10208-009-9045-5"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/2988450.2988454"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.330161"},{"key":"e_1_3_2_6_2","first-page":"249","volume-title":"Proceedings of the 13th International Conference on Artificial Intelligence and Statistics","author":"Glorot Xavier","year":"2010","unstructured":"Xavier Glorot and Yoshua Bengio. 2010. 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