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Existing sequential recommendation methods mainly focus on modeling the items\u2019 chronological relationships in each individual user behavior sequence, which may not be effective in making accurate and robust recommendations. On the one hand, the performance of existing sequential recommendation methods is usually sensitive to the length of a user\u2019s behavior sequence (i.e., the list of a user\u2019s historically interacted items). On the other hand, besides the context information in each individual user behavior sequence, the collaborative information among different users\u2019 behavior sequences is also crucial to make accurate recommendations. However, this kind of information is usually ignored by existing sequential recommendation methods. In this work, we propose a new sequential recommendation framework, which encodes the context information in each individual user behavior sequence as well as the collaborative information among the behavior sequences of different users, through building a local dependency graph for each item. We conduct extensive experiments to compare the proposed model with state-of-the-art sequential recommendation methods on five benchmark datasets. The experimental results demonstrate that the proposed model is able to achieve better recommendation performance than existing methods, by incorporating collaborative information.<\/jats:p>","DOI":"10.1145\/3649436","type":"journal-article","created":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T12:03:11Z","timestamp":1710504191000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["Collaborative Sequential Recommendations via Multi-view GNN-transformers"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9648-0982","authenticated-orcid":false,"given":"Tianze","family":"Luo","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9031-9696","authenticated-orcid":false,"given":"Yong","family":"Liu","sequence":"additional","affiliation":[{"name":"Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY), Nanyang Technological University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6565-3836","authenticated-orcid":false,"given":"Sinno Jialin","family":"Pan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,6,25]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543846"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462832"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462968"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462868"},{"key":"e_1_3_2_6_2","article-title":"Fastgcn: Fast learning with graph convolutional networks via importance sampling","author":"Chen Jie","year":"2018","unstructured":"Jie Chen, Tengfei Ma, and Cao Xiao. 2018. 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