Hierarchical Multi-Interest Co-Network For Coarse-Grained Ranking

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 represented. (2) Existing methods model the long-term and short-term behaviors together, ignoring the differences between them. This paper proposes a Hierarchical Multi-Interest Co-Network (HCN) to capture users' diverse interests in the coarse-grained ranking stage. Specifically, we design a hierarchical multi-interest extraction layer to update users' diverse interest centers iteratively. The multiple embedded vectors obtained in this way contain more information and represent the interests of users better in various aspects. Furthermore, we develop a Co-Interest Network to integrate users' long-term and short-term interests. Experiments on several real-world datasets and one large-scale industrial dataset show that HCN effectively outperforms the state-of-the-art methods. We deploy HCN into a large-scale real world E-commerce system and achieve extra 2.5\% improvements on GMV (Gross Merchandise Value).