Hierarchical Multi-Interest Co-Network For Coarse-Grained Ranking
Xu Yuan, Chen Xu, Qiwei Chen, Chao Li, Junfeng Ge, Wenwu Ou

TL;DR
This paper introduces a Hierarchical Multi-Interest Co-Network (HCN) that better captures diverse user interests in coarse-grained ranking, improving recommendation accuracy and increasing GMV in large-scale e-commerce systems.
Contribution
The paper proposes a novel hierarchical multi-interest extraction layer and a co-interest network to effectively model diverse and long-/short-term user interests, addressing limitations of previous methods.
Findings
HCN outperforms state-of-the-art methods on multiple datasets.
Deployment of HCN in a large-scale e-commerce system yields a 2.5% GMV increase.
The hierarchical interest extraction captures more comprehensive user preferences.
Abstract
In this era of information explosion, a personalized recommendation system is convenient for users to get information they are interested in. To deal with billions of users and items, large-scale online recommendation services usually consist of three stages: candidate generation, coarse-grained ranking, and fine-grained ranking. The success of each stage depends on whether the model accurately captures the interests of users, which are usually hidden in users' behavior data. Previous research shows that users' interests are diverse, and one vector is not sufficient to capture users' different preferences. Therefore, many methods use multiple vectors to encode users' interests. However, there are two unsolved problems: (1) The similarity of different vectors in existing methods is too high, with too much redundant information. Consequently, the interests of users are not fully…
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Taxonomy
TopicsRecommender Systems and Techniques · Text and Document Classification Technologies · Complex Network Analysis Techniques
