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Syst."],"published-print":{"date-parts":[[2024,1,31]]},"abstract":"<jats:p>\n            Multi-types of behaviors (e.g., clicking, carting, purchasing, etc.) widely exist in most real-world recommendation scenarios, which are beneficial to learn users\u2019 multi-faceted preferences. As dependencies are explicitly exhibited by the multiple types of behaviors, effectively modeling complex behavior dependencies is crucial for multi-behavior prediction. The state-of-the-art multi-behavior models learn behavior dependencies indistinguishably with all historical interactions as input. However, different behaviors may reflect different aspects of user preference, which means that some irrelevant interactions may play as noises to the target behavior to be predicted. To address the aforementioned limitations, we introduce multi-interest learning to the multi-behavior recommendation. More specifically, we propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning (CKML) framework to learn shared and behavior-specific interests for different behaviors. CKML introduces two advanced modules, namely\n            <jats:italic>Coarse-grained Interest Extracting (CIE)<\/jats:italic>\n            and\n            <jats:italic>Fine-grained Behavioral Correlation (FBC)<\/jats:italic>\n            , which work jointly to capture fine-grained behavioral dependencies. CIE uses knowledge-aware information to extract initial representations of each interest. FBC incorporates a dynamic routing scheme to further assign each behavior among interests. Empirical results on three real-world datasets verify the effectiveness and efficiency of our model in exploiting multi-behavior data.\n          <\/jats:p>","DOI":"10.1145\/3606369","type":"journal-article","created":{"date-parts":[[2023,6,28]],"date-time":"2023-06-28T12:11:33Z","timestamp":1687954293000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":37,"title":["Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2914-6527","authenticated-orcid":false,"given":"Chang","family":"Meng","sequence":"first","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6776-3029","authenticated-orcid":false,"given":"Ziqi","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beihang University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8616-0221","authenticated-orcid":false,"given":"Wei","family":"Guo","sequence":"additional","affiliation":[{"name":"Huawei Singapore Research Center, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8370-3873","authenticated-orcid":false,"given":"Yingxue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Huawei Technologies Canada, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6255-1535","authenticated-orcid":false,"given":"Haolun","family":"Wu","sequence":"additional","affiliation":[{"name":"McGill University, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7561-5646","authenticated-orcid":false,"given":"Chen","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8800-1483","authenticated-orcid":false,"given":"Dong","family":"Li","sequence":"additional","affiliation":[{"name":"Huawei Noah\u2019s Ark Lab, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0403-1923","authenticated-orcid":false,"given":"Xiu","family":"Li","sequence":"additional","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9224-2431","authenticated-orcid":false,"given":"Ruiming","family":"Tang","sequence":"additional","affiliation":[{"name":"Huawei Noah\u2019s Ark Lab, China"}]}],"member":"320","published-online":{"date-parts":[[2023,8,18]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"2020. 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