Improving Micro-video Recommendation via Contrastive Multiple Interests

  title={Improving Micro-video Recommendation via Contrastive Multiple Interests},
  author={Beibei Li and Beihong Jin and Jiageng Song and Yisong Yu and Y. Zheng and Wei Zhuo},
  journal={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  • Beibei Li, Beihong Jin, Wei Zhuo
  • Published 19 May 2022
  • Computer Science
  • Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
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… 

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