{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T10:38:27Z","timestamp":1768819107447,"version":"3.49.0"},"reference-count":50,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62172362"],"award-info":[{"award-number":["62172362"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Leading Expert of \u201cTen Thousands Talent Program\u201d of Zhejiang Province","award":["2021R52001"],"award-info":[{"award-number":["2021R52001"]}]},{"name":"MYbank, Ant Group"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Web"],"published-print":{"date-parts":[[2024,5,31]]},"abstract":"<jats:p>\n            Recommender systems are fundamental information filtering techniques to recommend content or items that meet users\u2019 personalities and potential needs. As a crucial solution to address the difficulty of user identification and unavailability of historical information, session-based recommender systems provide recommendation services that only rely on users\u2019 behaviors in the current session. However, most existing studies are not well-designed for modeling heterogeneous user behaviors and capturing the relationships between them in practical scenarios. To fill this gap, in this article, we propose a novel graph-based method, namely\n            <jats:bold>H<\/jats:bold>\n            eterogeneous\n            <jats:bold>I<\/jats:bold>\n            nformation\n            <jats:bold>C<\/jats:bold>\n            rossing on\n            <jats:bold>G<\/jats:bold>\n            raphs (HICG). HICG utilizes multiple types of user behaviors in the sessions to construct heterogeneous graphs, and captures users\u2019 current interests with their long-term preferences by effectively crossing the heterogeneous information on the graphs. In addition, we also propose an enhanced version, named HICG-CL, which incorporates the contrastive learning (CL) technique to enhance item representation ability. By utilizing the item co-occurrence relationships across different sessions, HICG-CL improves the recommendation performance of HICG. We conduct extensive experiments on three real-world recommendation datasets, and the results verify that (i) HICG achieves state-of-the-art performance by utilizing multiple types of behaviors on the heterogeneous graph. (ii) HICG-CL further significantly improves the recommendation performance of HICG by the proposed contrastive learning module.\n          <\/jats:p>","DOI":"10.1145\/3572407","type":"journal-article","created":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T12:22:52Z","timestamp":1670934172000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Heterogeneous Information Crossing on Graphs for Session-Based Recommender Systems"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5483-0366","authenticated-orcid":false,"given":"Xiaolin","family":"Zheng","sequence":"first","affiliation":[{"name":"College of Computer Science, Zhejiang University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0953-7720","authenticated-orcid":false,"given":"Rui","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer Science, Zhejiang University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9957-7325","authenticated-orcid":false,"given":"Zhongxuan","family":"Han","sequence":"additional","affiliation":[{"name":"College of Computer Science, Zhejiang University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1419-964X","authenticated-orcid":false,"given":"Chaochao","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science, Zhejiang University"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3764-737X","authenticated-orcid":false,"given":"Linxun","family":"Chen","sequence":"additional","affiliation":[{"name":"MYbank, Ant Group"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8051-1278","authenticated-orcid":false,"given":"Bing","family":"Han","sequence":"additional","affiliation":[{"name":"MYbank, Ant Group"}]}],"member":"320","published-online":{"date-parts":[[2024,1,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/MLSP.2016.7738886"},{"key":"e_1_3_2_3_2","first-page":"57","volume-title":"Proceedings of the 1st Workshop on Temporal Reasoning in Recommender Systems Co-Located with 11th International Conference on Recommender Systems, RecSys 2017","author":"Bogina Veronika","year":"2017","unstructured":"Veronika Bogina and Tsvi Kuflik. 2017. Incorporating dwell time in session-based recommendations with recurrent neural networks. In Proceedings of the 1st Workshop on Temporal Reasoning in Recommender Systems Co-Located with 11th International Conference on Recommender Systems, RecSys 2017. 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