{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:42:11Z","timestamp":1787017331834,"version":"build-2736575974"},"reference-count":58,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T00:00:00Z","timestamp":1669766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Web"],"published-print":{"date-parts":[[2022,11,30]]},"abstract":"<jats:p>\n                    The explosive growth of e-commerce and online service has led to the development of recommender system. Aiming to provide a list of items to meet a user\u2019s personalized need by analyzing his\/her interaction\n                    <jats:xref ref-type=\"fn\">\n                      <jats:sup>1<\/jats:sup>\n                    <\/jats:xref>\n                    history, recommender system has been widely studied in academic and industrial communities. Different from conventional recommender systems, sequential recommender systems attempt to capture the pattern of users\u2019 sequential behaviors and the evolution of users\u2019 preferences. Most of the existing sequential recommendation models only focus on user interaction sequence, but neglect item interaction sequence. An item interaction sequence also contains rich contextual information for capturing the item\u2019s dynamic characteristic, since an item\u2019s dynamic characteristic can be reflected by the users who interact with it in a period. Furthermore, existing dual sequential models use the same method to handle the user interaction sequence and item interaction sequence, and do not consider their different characteristics. Hence, we propose a novel\n                    <jats:bold>\n                      <jats:underline>D<\/jats:underline>\n                      ouble\n                      <jats:underline>A<\/jats:underline>\n                      ttention\n                      <jats:underline>C<\/jats:underline>\n                      onvolution\n                      <jats:underline>N<\/jats:underline>\n                      eural\n                      <jats:underline>N<\/jats:underline>\n                      etwork (DACNN)\n                    <\/jats:bold>\n                    , which incorporates user interaction sequence and item interaction sequence into an integrated neural network framework. DACNN leverages the strength of attention mechanism to capture the temporary suitability and adopts CNN to extract local sequential features. Experimental evaluations on the real datasets show that DACNN outperforms the baseline approaches.\n                  <\/jats:p>","DOI":"10.1145\/3555350","type":"journal-article","created":{"date-parts":[[2022,8,9]],"date-time":"2022-08-09T07:41:39Z","timestamp":1660030899000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Double Attention Convolutional Neural Network for Sequential Recommendation"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3710-5979","authenticated-orcid":false,"given":"Qi","family":"Chen","sequence":"first","affiliation":[{"name":"HuaZhong University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6984-1914","authenticated-orcid":false,"given":"Guohui","family":"Li","sequence":"additional","affiliation":[{"name":"HuaZhong University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6020-0416","authenticated-orcid":false,"given":"Quan","family":"Zhou","sequence":"additional","affiliation":[{"name":"HuaZhong University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1788-1767","authenticated-orcid":false,"given":"Si","family":"Shi","sequence":"additional","affiliation":[{"name":"HuaZhong University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8534-5048","authenticated-orcid":false,"given":"Deqing","family":"Zou","sequence":"additional","affiliation":[{"name":"HuaZhong University of Science and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,12,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"key":"e_1_3_2_3_2","volume-title":"ICLR","author":"Bahdanau Dzmitry","year":"2015","unstructured":"Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. 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