  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.
