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Inf. Syst."],"published-print":{"date-parts":[[2022,7,31]]},"abstract":"<jats:p>\n            The popularity of social media platforms results in a huge volume of online conversations produced every day. To help users better engage in online conversations, this article presents a novel framework to automatically recommend conversations to users based on what they said and how they behaved in their chatting histories. While prior work mostly focuses on post-level recommendation, we aim to explore conversation context and model the interaction patterns therein. Furthermore, to characterize personal interests from interleaving user interactions, we learn (1)\n            <jats:italic>global interactions<\/jats:italic>\n            , represented by topic and discourse word clusters to reflect users\u2019 content and pragmatic preferences, and (2)\n            <jats:italic>local interactions<\/jats:italic>\n            , encoding replying relations and chronological order of conversation turns to characterize users\u2019 prior behavior. Built on collaborative filtering, our model captures global interactions via discovering word distributions to represent users\u2019 topical interests and discourse behaviors, while local interactions are explored with graph-structured networks exploiting both reply structure and temporal features. Extensive experiments on three datasets from Twitter and Reddit show that our model coupling global and local interactions significantly outperforms the state-of-the-art model. Further analyses show that our model is able to capture meaningful features from global and local interactions, which results in its superior performance in conversation recommendation.\n          <\/jats:p>","DOI":"10.1145\/3473970","type":"journal-article","created":{"date-parts":[[2021,11,17]],"date-time":"2021-11-17T21:53:46Z","timestamp":1637186026000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Modeling Global and Local Interactions for Online Conversation Recommendation"],"prefix":"10.1145","volume":"40","author":[{"given":"Xingshan","family":"Zeng","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Kowloon, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingzhi","family":"Wang","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kam-Fai","family":"Wong","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,11,17]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/1134271.1134277"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/1341531.1341557"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/2911451.2914760"},{"key":"e_1_3_2_5_2","first-page":"519","volume-title":"Proceedings of the 10th International Conference on Web and Social Media","author":"Alvarez-Melis David","year":"2016","unstructured":"David Alvarez-Melis and Martin Saveski. 2016. 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