{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T15:11:32Z","timestamp":1744297892194,"version":"3.37.3"},"reference-count":52,"publisher":"Association for Computing Machinery (ACM)","issue":"1","funder":[{"DOI":"10.13039\/501100013105","name":"Shanghai Rising-Star Program","doi-asserted-by":"crossref","award":["23QA1403100"],"id":[{"id":"10.13039\/501100013105","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["72192832"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Natural Science Foundation of Shanghai","award":["21ZR1421900"]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Web"],"published-print":{"date-parts":[[2024,2,29]]},"abstract":"\n Session-based recommendation, which has witnessed a booming interest recently, focuses on predicting a user\u2019s next interested item(s) based on an anonymous session. Most existing studies adopt complex deep learning techniques (e.g., graph neural networks) for effective session-based recommendation. However, they merely address\n co-occurrence<\/jats:italic>\n between items, but fail to distinguish a\n causality<\/jats:italic>\n and\n correlation<\/jats:italic>\n relationship. Considering the varied interpretations and characteristics of causality and correlation relationships between items, in this study, we propose a novel method denoted as CGSR by jointly modeling causality and correlation relationships between items. In particular, we construct cause, effect, and correlation graphs from sessions by simultaneously considering the false causality problem. We further design a graph neural network\u2013based method for session-based recommendation. To conclude, we strive to explore the relationship between items from specific \u201ccausality\u201d (directed) and \u201ccorrelation\u201d (undirected) perspectives. Extensive experiments on three datasets show that our model outperforms other state-of-the-art methods in terms of recommendation accuracy. Moreover, we further propose an explainable framework on CGSR and demonstrate the explainability of our model via case studies on an Amazon dataset.\n <\/jats:p>","DOI":"10.1145\/3593313","type":"journal-article","created":{"date-parts":[[2023,6,5]],"date-time":"2023-06-05T10:01:50Z","timestamp":1685959310000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Causality and Correlation Graph Modeling for Effective and Explainable Session-Based Recommendation"],"prefix":"10.1145","volume":"18","author":[{"given":"Huizi","family":"Wu","sequence":"first","affiliation":[{"name":"RIIS & SIME, Shanghai University of Finance and Economics, China"}]},{"given":"Cong","family":"Geng","sequence":"additional","affiliation":[{"name":"RIIS & SIME, Shanghai University of Finance and Economics, China"}]},{"given":"Hui","family":"Fang","sequence":"additional","affiliation":[{"name":"RIIS & SIME, Shanghai University of Finance and Economics, China"}]}],"member":"320","published-online":{"date-parts":[[2023,10,11]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240360"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403170"},{"key":"e_1_3_2_4_2","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/978-3-030-73280-6_5","volume-title":"Proceedings of the 13th Asian Conference on Intelligent Information and Database Systems","author":"Dobrovolny Michal","year":"2021","unstructured":"Michal Dobrovolny, Ali Selamat, and Ondrej Krejcar. 2021. Session based recommendations using recurrent neural networks-long short-term memory. In Proceedings of the 13th Asian Conference on Intelligent Information and Database Systems. Springer, 53\u201365."},{"key":"e_1_3_2_5_2","first-page":"152","volume-title":"Proceedings of the 11th ACM Conference on Recommender Systems","author":"Donkers Tim","year":"2017","unstructured":"Tim Donkers, Benedikt Loepp, and J\u00fcrgen Ziegler. 2017. Sequential user-based recurrent neural network recommendations. 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