2022/05/19 by Beibei Li, Li, Beibei, Beihong Jin +9 · 3 citations
Computer Science · #FOS: Computer and information sciences #Human Pose and Action Recognition #Information Retrieval (cs.IR) #Recommender Systems and Techniques #Video Analysis and Summarization #cs.IR
paper · pdf · doi:10.48550/arxiv.2205.09593
arxiv created 2022/05/19 · openalex publication_date 2022/05/19 · arxiv updated 2022/05/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With the rapid increase of micro-video creators and viewers, how to make personalized recommendations from a large number of candidates to viewers begins to attract more and more attention. However, existing micro-video recommendation models rely on expensive multi-modal information and learn an overall interest embedding that cannot reflect the user's multiple interests in micro-videos. Recently, contrastive learning provides a new opportunity for refining the existing recommendation techniques. Therefore, in this paper, we propose to extract contrastive multi-interests and devise a micro-video recommendation model CMI. Specifically, CMI learns multiple interest embeddings for each user from his/her historical interaction sequence, in which the implicit orthogonal micro-video categories are used to decouple multiple user interests. Moreover, it establishes the contrastive multi-interest loss to improve the robustness of interest embeddings and the performance of recommendations. The results of experiments on two micro-video datasets demonstrate that CMI achieves state-of-the-art performance over existing baselines.