{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T01:43:57Z","timestamp":1769651037590,"version":"3.49.0"},"reference-count":51,"publisher":"Association for Computing Machinery (ACM)","issue":"2","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["72074036 and 62072060"],"award-info":[{"award-number":["72074036 and 62072060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Natural Science Foundation of Chongqing, China","award":["CSTB2024NSCQ-MSX0701"],"award-info":[{"award-number":["CSTB2024NSCQ-MSX0701"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>\n                    Multi-behavior recommendation leverages multiple user-item interaction information to alleviate data sparsity. Although different types of user-item interactions are temporally mutually exclusive, the sequence of behavioral interactions consisting of multi-level positive feedback signals contains rich information. However, most existing studies have unilaterally focused on the positive utility of auxiliary behaviors, ignoring multi-level user preference information. Effectively fusing multi-behavioral data and better modeling behavioral dependencies are urgent problems that need to be addressed for multi-behavior recommendation. We propose the\n                    <jats:underline>p<\/jats:underline>\n                    arallel learning of\n                    <jats:underline>p<\/jats:underline>\n                    ositive and\n                    <jats:underline>n<\/jats:underline>\n                    egative interests with an\n                    <jats:underline>a<\/jats:underline>\n                    uxiliary-view\n                    <jats:underline>r<\/jats:underline>\n                    epresentation\n                    <jats:underline>e<\/jats:underline>\n                    nhancement (PPN-ARE) scheme for multi-level user interest learning based on multi-behavioral interaction sequences. Specifically, multi-level positive and negative feedback view chains are constructed from multi-behavioral sequence data to learn multi-level user interests. User preference evolution is simulated during multi-behavior interactions using residual connections, and the shortcomings of the cascading structure used for higher-order graph learning are analytically highlighted. The influence of low-quality embeddings of auxiliary behaviors is filtered, and the learning of target behaviors is optimized by designing a representation enhancement layer. Finally, the model is optimized using a multi-task training framework. The experimental results indicate that PPN-ARE significantly improved over the state-of-the-art (SOTA). The open source code is available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/lhybq\/PPN-ARE\">https:\/\/github.com\/lhybq\/PPN-ARE<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3786341","type":"journal-article","created":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T13:04:07Z","timestamp":1766495047000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Representation-Enhanced Cascading Multi-Level Interest Learning for Multi-Behavior Recommendation"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0411-3795","authenticated-orcid":false,"given":"Hu","family":"Liu","sequence":"first","affiliation":[{"name":"School of Big Data &amp; Software Engineering, Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7179-1626","authenticated-orcid":false,"given":"Xiao","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Southwest University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0839-8773","authenticated-orcid":false,"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Big Data &amp; Software Engineering, Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7460-198X","authenticated-orcid":false,"given":"Fengji","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, The University of Sydney, Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6561-560X","authenticated-orcid":false,"given":"Junhao","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Big Data &amp; Software Engineering, Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3063-9425","authenticated-orcid":false,"given":"Hongyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Big Data &amp; Software Engineering, Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,1,28]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539170"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3373807"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3673244"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583439"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00113"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/464"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2958808"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/285"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371827"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2017.10.005"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330670"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557236"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401072"},{"key":"e_1_3_2_17_2","unstructured":"Thomas N. 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