{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T16:16:58Z","timestamp":1778948218368,"version":"3.51.4"},"reference-count":52,"publisher":"Association for Computing Machinery (ACM)","issue":"4","funder":[{"name":"National Key Research and Development Program of China","award":["2024YFF0617702, 2019YFB1405302"],"award-info":[{"award-number":["2024YFF0617702, 2019YFB1405302"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U22A2025, 62232007, U23A20309, 61872072"],"award-info":[{"award-number":["U22A2025, 62232007, U23A20309, 61872072"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100013314","name":"111 Project","doi-asserted-by":"crossref","award":["B16009"],"award-info":[{"award-number":["B16009"]}],"id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2026,5,31]]},"abstract":"<jats:p>\n                    Heterogeneous behavioral data provides comprehensive insights into user intentions and decision-making patterns. Contemporary multi-behavior recommendation models, which leverage such data to infer user preferences, typically capture high-order collaborative signals through graph neural networks on a multi-behavior heterogeneous graph or multiple behavior-specific subgraphs. However, auxiliary behaviors (e.g., view, cart) inherently contain noise that can mislead target behavior (e.g., purchase) prediction, and the incorporation of high-order collaborative signals further amplify such noise. Moreover, these approaches fail to adequately explore cross-behavior item dependencies, leading to inadequate modeling of dependencies across heterogeneous behaviors. To address these limitations, we propose\n                    <jats:bold>Cross-behavior Item DEpendency modeling for multi-behavior Recommendation (CIDER)<\/jats:bold>\n                    , a novel framework that explicitly models item dependencies across multiple types of behaviors for target behavior prediction (e.g., purchase). Specifically, our framework introduces the\n                    <jats:bold>Hierarchical Behavior Sequence (HBS)<\/jats:bold>\n                    , a data structure to systematically organize multi-behavior user\u2013item interactions. Based on the HBS, we design a\n                    <jats:bold>Cross-behavior Item Dependency Modeling (CIDM)<\/jats:bold>\n                    module coupled with a multi-behavior cascading learning scheme to capture item-level dependencies. To enhance the robustness of the representations learned from the CIDM module, we develop an HBS-based denoising module that filters out noise inherent in auxiliary behaviors. Empirical evaluation on three benchmark datasets demonstrates the effectiveness of our model in harnessing multi-behavior data. The implementation is publicly available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/SunJianier\/CIDER\">https:\/\/github.com\/SunJianier\/CIDER<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3801153","type":"journal-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T14:11:49Z","timestamp":1773151909000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-behavior Item Dependency Modeling for Multi-behavior Recommendation"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8539-5446","authenticated-orcid":false,"given":"Jian","family":"Sun","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9855-6300","authenticated-orcid":false,"given":"Gang","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang, China and Key Laboratory of Intelligent Computing in Medical Image (Northeastern University), Ministry of Education, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7292-2976","authenticated-orcid":false,"given":"Jiayao","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7103-2795","authenticated-orcid":false,"given":"Yifei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6184-4771","authenticated-orcid":false,"given":"Xiaochun","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2694-1023","authenticated-orcid":false,"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Northeastern University, Shenyang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5161-0611","authenticated-orcid":false,"given":"Yatong","family":"Sun","sequence":"additional","affiliation":[{"name":"Software College, Northeastern University, Shenyang, 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