Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3477495.3531861 | SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval |
Keywords | DocType | Citations |
Micro-video recommendation, Contrastive learning, Multi-interest learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 10 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Beibei Li | 1 | 0 | 1.69 |
| Beihong Jin | 2 | 405 | 49.23 |
| Jiageng Song | 3 | 0 | 0.34 |
| Yisong Yu | 4 | 0 | 0.34 |
| Yiyuan Zheng | 5 | 0 | 0.34 |
| Wei Zhuo | 6 | 0 | 1.35 |