{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T18:06:51Z","timestamp":1764785211891,"version":"3.38.0"},"reference-count":36,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIC"],"published-print":{"date-parts":[[2023,10,13]]},"abstract":"<jats:p>Session-based recommendation aims at predicting the next behavior when the current interaction sequence is given. Recent advances evaluate the effectiveness of dual cross-domain information for the session-based recommendation. However, we discover that accurately modeling the session representations is still a challenging problem due to the complexity of preference interactions in the cross-domain, and various methods are proposed to only model the common features of cross-domain, while ignoring the specific features and enhanced features for the dual cross-domain. Without modeling the complete features, the existing methods suffer from poor recommendation accuracy. Therefore, we propose an end-to-end dual cross-domain with multi-channel interaction model (DCMI), which utilizes dual cross-domain session information and multiple preference interaction encoders, for session-based recommendation. In DCMI, we apply a graph neural network to generate the session global preference and local preference. Then, we design a cross-preference interaction module to capture the common, specific, and enhanced features for cross-domain sessions with local preferences and global preferences. Finally, we combine multiple preferences with a bilinear fusion mechanism to characterize and make recommendations. Experimental results on the Amazon dataset demonstrate the superiority of the DCMI model over the state-of-the-art methods.<\/jats:p>","DOI":"10.3233\/aic-230084","type":"journal-article","created":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T16:04:58Z","timestamp":1696953898000},"page":"341-359","source":"Crossref","is-referenced-by-count":3,"title":["Dual cross-domain session-based recommendation with multi-channel integration"],"prefix":"10.1177","volume":"36","author":[{"given":"Jinjin","family":"Zhang","sequence":"first","affiliation":[{"name":"Xi\u2019an Technological University, Xuefu Middle Road No.2, Xi\u2019an, Shannxi, China"}]},{"given":"Xiang","family":"Hua","sequence":"additional","affiliation":[{"name":"Xi\u2019an Technological University, Xuefu Middle Road No.2, Xi\u2019an, Shannxi, China"}]},{"given":"Peng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Xi\u2019an High-Tech Research Institution, Tongxin Road No.2, Xi\u2019an, Shannxi, China"}]},{"given":"Kai","family":"Kang","sequence":"additional","affiliation":[{"name":"Xi\u2019an High-Tech Research Institution, Tongxin Road No.2, Xi\u2019an, Shannxi, China"}]}],"member":"179","reference":[{"key":"10.3233\/AIC-230084_ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462866"},{"key":"10.3233\/AIC-230084_ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462866"},{"key":"10.3233\/AIC-230084_ref3","doi-asserted-by":"publisher","DOI":"10.1145\/1864708.1864770"},{"key":"10.3233\/AIC-230084_ref4","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/342"},{"key":"10.3233\/AIC-230084_ref5","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2206.08088"},{"key":"10.3233\/AIC-230084_ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"10.3233\/AIC-230084_ref7","unstructured":"B.\u00a0Hidasi, A.\u00a0Karatzoglou, L.\u00a0Baltrunas and D.\u00a0Tikk, Session-based recommendations with recurrent neural networks, in: 4th 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