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Syst."],"published-print":{"date-parts":[[2023,7,31]]},"abstract":"<jats:p>Location-based social networks (LBSNs) have become a popular platform for users to share their activities with friends and families, which provide abundant information for us to study issues of group venue recommendation by utilizing the characteristics of check-in data. Although there are some studies on group recommendation for venues, few studies consider the group\u2019s venue preference in different temporal patterns. In this article, we discover that the group\u2019s activity venue has a temporal effect, that is, the group\u2019s preference for the activity venue is different at different times. For example, a couple of lovers prefer to travel to tropical regions in winter and relax in bars in the evening. Based on this discovery, we present a Time-aware Multi-pattern (TaMp) topic model to capture the group\u2019s interest in the activity venue in multiple temporal patterns (including the daily pattern, the weekly pattern, the monthly pattern, and the quarterly pattern). The TaMp model takes into account the topic, members, temporality, and venue information of group activities and the latent relations among them, especially the strong correlation between the activity time and the corresponding activity venue. Then, we propose a group venue recommendation method based on the TaMp model. In addition, an improved grouping algorithm (iGA) in LBSNs is put forward to enhance the rationality of grouping and the accuracy of group venue recommendation. We conduct comprehensive experiments to evaluate the performance of TaMp on two real-world datasets. The results show that our proposed method outperforms the state-of-the-art group venue recommendation and demonstrates the significance of temporal effects in explaining group activities.<\/jats:p>","DOI":"10.1145\/3564280","type":"journal-article","created":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T12:54:46Z","timestamp":1664196886000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Exploring Time-aware Multi-pattern Group Venue Recommendation in LBSNs"],"prefix":"10.1145","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2180-5986","authenticated-orcid":false,"given":"Bi","family":"Liang","sequence":"first","affiliation":[{"name":"Beijing Key Lab of Intelligent Telecommunications Software and Multimedia &amp; School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4477-8020","authenticated-orcid":false,"given":"Xiangwu","family":"Meng","sequence":"additional","affiliation":[{"name":"Beijing Key Lab of Intelligent Telecommunications Software and Multimedia &amp; School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1805-8342","authenticated-orcid":false,"given":"Yujie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Key Lab of Intelligent Telecommunications Software and Multimedia &amp; School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2023,2,7]]},"reference":[{"issue":"1","key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"754","DOI":"10.14778\/1687627.1687713","article-title":"Group recommendation: Semantics and efficiency","volume":"2","author":"Amer-Yahia Sihem","year":"2010","unstructured":"Sihem Amer-Yahia, Senjuti Basu Roy, Ashish Chawlat, Gautam Das, and Cong Yu. 2010. 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