Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
Chao Li, Zhiyuan Liu, Mengmeng Wu, Yuchi Xu, Pipei Huang, Huan Zhao,, Guoliang Kang, Qiwei Chen, Wei Li, Dik Lun Lee

TL;DR
This paper introduces MIND, a multi-interest recommendation model using dynamic routing to better capture diverse user interests, significantly improving recommendation accuracy and deployed at Tmall's mobile app.
Contribution
The paper proposes a novel multi-interest network with capsule routing and label-aware attention, advancing user interest modeling for large-scale industrial recommender systems.
Findings
MIND outperforms state-of-the-art methods on public benchmarks.
MIND achieves superior recommendation accuracy in large-scale industrial data.
Deployed at Tmall, MIND handles major online traffic effectively.
Abstract
Industrial recommender systems usually consist of the matching stage and the ranking stage, in order to handle the billion-scale of users and items. The matching stage retrieves candidate items relevant to user interests, while the ranking stage sorts candidate items by user interests. Thus, the most critical ability is to model and represent user interests for either stage. Most of the existing deep learning-based models represent one user as a single vector which is insufficient to capture the varying nature of user's interests. In this paper, we approach this problem from a different view, to represent one user with multiple vectors encoding the different aspects of the user's interests. We propose the Multi-Interest Network with Dynamic routing (MIND) for dealing with user's diverse interests in the matching stage. Specifically, we design a multi-interest extractor layer based on…
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Taxonomy
TopicsRecommender Systems and Techniques · Caching and Content Delivery · Advanced Graph Neural Networks
