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This shortcoming prevents them from explicating and utilizing task information, which is shown to be instrumental in many information retrieval applications. To overcome this limitation, we propose a new Task-based Graph Neural Network model (TGNN) focusing on identifying users\u2019 underlying tasks within their temporal multi-behavior, specifically in each session. The model consists of three modules: (1) a sequential meta-path module that captures a temporal sequence of users\u2019 behaviors; (2) a graph neural network layer that models the relationships between different information items and users into task representations; and (3) a recommendation layer that utilizes a collaborative filtering method to generate top-N recommendations based on the model\u2019s comprehension of users\u2019 tasks. The novelty of our approach lies in understanding users\u2019 tasks through their temporal behavior, enabling more accurate personalization. The results of evaluative experiments on three publicly available datasets demonstrate the effectiveness of our task-based recommendation model compared to 10 baselines and indicate a promising research direction for task-oriented recommender systems.<\/jats:p>","DOI":"10.1145\/3736159","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T05:26:57Z","timestamp":1747200417000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Towards More Personalized Recommendations by Modeling Users? Temporal Behaviors with Task-Based Graph Neural Network (TGNN)"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6142-0637","authenticated-orcid":false,"given":"Maryam","family":"Amirizaniani","sequence":"first","affiliation":[{"name":"Information School, University of Washington","place":["Seattle, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2551-211X","authenticated-orcid":false,"given":"Shawon","family":"Sarkar","sequence":"additional","affiliation":[{"name":"Information School, University of Washington","place":["Seattle, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3797-4293","authenticated-orcid":false,"given":"Chirag","family":"Shah","sequence":"additional","affiliation":[{"name":"Information School, University of Washington","place":["Seattle, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,22]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371883"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121969"},{"key":"e_1_3_2_4_2","doi-asserted-by":"crossref","unstructured":"Tong Chen Hongzhi Yin Hongxu Chen Rui Yan Quoc Viet Hung Nguyen and Xue Li. 2019. 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