HimiRec: Modeling Hierarchical Multi-interest for Recommendation

Industrial recommender systems usually consist of the retrieval stage and the ranking stage, to handle the billion-scale of users and items. The retrieval stage retrieves candidate items relevant to user interests for recommendations and has attracted much attention. Frequently, users show hierarchical multi-interests reflected in a heavy user of a certain NBA team Golden State Warriors in Sports, who is also a light user of almost the whole Animation. Both Sports and Animation are at the same level. However, most existing methods implicitly learn this hierarchical difference, making more fine-grained interest information to be averaged and limiting detailed understanding of the user's different needs in heavy interests and other light interests. Therefore, we propose a novel two-stage approach to explicitly modeling hierarchical multi-interest for recommendation in this work. In the first hierarchical multi-interest mining stage, the hierarchical clustering and transformer-based model adaptively generate circles or sub-circles that users are interested in. In the second stage, the partition of retrieval space allows the EBR models to only deal with items within each circle and accurately capture user's refined interests. Experimental results show that the proposed approach achieves state-of-the-art performance. Our framework has also successfully deployed at Lofter (one of the largest derivative content communities with 10 million monthly active users) for over four months.