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They are used to characterize users\u2019 preferences from their historical interactions and recommend micro-videos accordingly. Existing works largely leverage the multi-modal contents of micro-videos to enhance recommendation performance. However, limited efforts have been made to understand users\u2019 complex behavior patterns, including their long- and short-term interests, as well as their temporal diversity preferences. In micro-video recommendation scenarios, users tend to have both stable long-term interests and dynamic short-term interests, and may feel tired after incessantly receiving numerous similar recommendations. In this paper, we propose a <jats:bold>T<\/jats:bold>emporal <jats:bold>D<\/jats:bold>iversity-aware micro-<jats:bold>video<\/jats:bold><jats:bold>rec<\/jats:bold>ommender (TD-VideoRec) for user behavior modeling, simultaneously capturing users\u2019 long- and short-term preferences. Specifically, we first adopt a user-centric attention mechanism to cope with long-term interests. Then, we utilize an attention network on top of a long-short term memory network to obtain users\u2019 short-term interests. Finally, a temporal diversity coefficient is introduced to characterize the temporal diversity preferences of users\u2019 click behaviors. The value of the coefficient depends on the distinction between users\u2019 long- and short-term interests extracted by vector orthogonal projection. Extensive experiments on two real-world datasets demonstrate that TD-VideoRec outperforms state-of-the-art methods.<\/jats:p>","DOI":"10.1007\/s11063-024-11652-7","type":"journal-article","created":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T06:02:28Z","timestamp":1717394548000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Temporal Diversity-Aware Micro-Video Recommendation with Long- and Short-Term Interests Modeling"],"prefix":"10.1007","volume":"56","author":[{"given":"Pan","family":"Gu","sequence":"first","affiliation":[]},{"given":"Haiyang","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Dongjing","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Dongjin","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Guandong","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,3]]},"reference":[{"key":"11652_CR1","doi-asserted-by":"crossref","unstructured":"Lv F, Jin T, Yu C, Sun F, Lin Q, Yang K, Ng W (2019) Sdm: sequential deep matching model for online large-scale recommender system. 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