{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:06:36Z","timestamp":1753884396830,"version":"3.41.2"},"reference-count":34,"publisher":"World Scientific Pub Co Pte Ltd","issue":"09","funder":[{"name":"University Key Scientific Research Project of Anhui Province","award":["2022AH050821"],"award-info":[{"award-number":["2022AH050821"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076006"],"award-info":[{"award-number":["62076006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:p> Multi-interest sequential recommendations leverage users\u2019 historical behavior to provide recommendations that match multiple interests. Most of these methods have not fully extracted higher-order information hidden in users\u2019 interactions and have overlooked the multiple features of items. To this end, this paper proposes a multi-interest model called \u201cmulti-interest sequential recommendation with simplified graph convolution and item multi-features (SGCMF)\u201d. Firstly, a simplified graph convolution module is designed based on bipartite graphs, which utilizes mean pooling to aggregate neighboring information and employs a feedforward neural network (FNN) for nonlinear transformations and combinations. This method reduces redundant information and captures higher-order relationships, thereby simplifying the complexity of modeling high-order interactions and improving prediction accuracy. Secondly, an item multi-feature extraction module is proposed, which represents item features with multiple vectors, and analyzes each feature from multiple perspectives while preserving important relationships between features. The model correlates multiple features of the item with user interests, thereby achieving a fine-grained analysis of user interests. Extensive experiments are conducted on five real-world scenarios, and the results are compared with state-of-the-art methods. The experimental results show that SGCMF outperforms other baselines. <\/jats:p>","DOI":"10.1142\/s0218001424590092","type":"journal-article","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T16:11:45Z","timestamp":1717085505000},"source":"Crossref","is-referenced-by-count":0,"title":["Multi-Interest Sequential Recommendation with Simplified Graph Convolution and Multiple Item Features"],"prefix":"10.1142","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3966-2031","authenticated-orcid":false,"given":"Kelei","family":"Sun","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan Anhui 232001, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0453-9787","authenticated-orcid":false,"given":"Mengqi","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan Anhui 232001, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4419-0825","authenticated-orcid":false,"given":"Huaping","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan Anhui 232001, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6870-6716","authenticated-orcid":false,"given":"Yingying","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan Anhui 232001, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5898-5770","authenticated-orcid":false,"given":"Sai","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan Anhui 232001, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2024,6,29]]},"reference":[{"key":"S0218001424590092BIB001","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3501396"},{"key":"S0218001424590092BIB002","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357895"},{"key":"S0218001424590092BIB003","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557464"},{"key":"S0218001424590092BIB004","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"S0218001424590092BIB005","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403344"},{"key":"S0218001424590092BIB006","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/197"},{"key":"S0218001424590092BIB007","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482242"},{"key":"S0218001424590092BIB008","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2020.3031329"},{"key":"S0218001424590092BIB009","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358113"},{"key":"S0218001424590092BIB010","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"S0218001424590092BIB011","first-page":"1024","volume-title":"in Advances Conf. 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