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Syst."],"published-print":{"date-parts":[[2024,3,31]]},"abstract":"<jats:p>\n                    Session-based recommendation (SBR) systems aim to utilize the user\u2019s short-term behavior sequence to predict the next item without the detailed user profile.\n                    <jats:styled-content style=\"color:#000000\">Most recent works try to model the user preference by treating the sessions as between-item transition graphs and utilize various graph neural networks (GNNs) to encode the representations of pair-wise relations among items and their neighbors. Some of the existing GNN-based models mainly focus on aggregating information from the view of spatial graph structure, which ignores the temporal relations within neighbors of an item during message passing and the information loss results in a sub-optimal problem. Other works embrace this challenge by incorporating additional temporal information but lack sufficient interaction between the spatial and temporal patterns. To address this issue, inspired by the uniformity and alignment properties of contrastive learning techniques, we propose a novel framework called Session-based Recommendation with Spatio-temporal Contrastive Learning-enhanced GNNs (RESTC). The idea is to supplement the GNN-based main supervised recommendation task with the temporal representation via an auxiliary cross-view contrastive learning mechanism. Furthermore, a novel global collaborative filtering graph embedding is leveraged to enhance the spatial view in the main task.<\/jats:styled-content>\n                    Extensive experiments demonstrate the significant performance of RESTC compared with the state-of-the-art baselines. We release our source code at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/SUSTechBruce\/RESTC-Source-code\">https:\/\/github.com\/SUSTechBruce\/RESTC-Source-code<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3626091","type":"journal-article","created":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T11:53:35Z","timestamp":1696420415000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":36,"title":["Spatio-temporal Contrastive Learning-enhanced GNNs for Session-based Recommendation"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2790-0290","authenticated-orcid":false,"given":"Zhongwei","family":"Wan","sequence":"first","affiliation":[{"name":"The Ohio State University, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9610-9526","authenticated-orcid":false,"given":"Xin","family":"Liu","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1501-9914","authenticated-orcid":false,"given":"Benyou","family":"Wang","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9514-0708","authenticated-orcid":false,"given":"Jiezhong","family":"Qiu","sequence":"additional","affiliation":[{"name":"Tencent, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6015-0156","authenticated-orcid":false,"given":"Boyu","family":"Li","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5130-3237","authenticated-orcid":false,"given":"Ting","family":"Guo","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5892-8608","authenticated-orcid":false,"given":"Guangyong","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang Lab, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6815-0879","authenticated-orcid":false,"given":"Yang","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,11,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/279943.279962"},{"key":"e_1_3_2_3_2","first-page":"1597","volume-title":"Proceedings of the ICML","volume":"119","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. 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