Diversity Regularized Interests Modeling for Recommender Systems

TL;DR

This paper introduces Diversity Regularized Interests Modeling (DRIM), a novel approach using capsule networks and regularization strategies to generate diverse user interest vectors, improving recommendation accuracy in e-commerce settings.

Contribution

The paper proposes a new method combining capsule networks and diversity regularization to generate distinct user interest vectors for better recommendations.

Findings

Model captures diverse user interests effectively.

Outperforms existing multi-interest methods on public and industrial datasets.

Demonstrates improved recommendation accuracy.

Abstract

With the rapid development of E-commerce and the increase in the quantity of items, users are presented with more items hence their interests broaden. It is increasingly difficult to model user intentions with traditional methods, which model the user's preference for an item by combining a single user vector and an item vector. Recently, some methods are proposed to generate multiple user interest vectors and achieve better performance compared to traditional methods. However, empirical studies demonstrate that vectors generated from these multi-interests methods are sometimes homogeneous, which may lead to sub-optimal performance. In this paper, we propose a novel method of Diversity Regularized Interests Modeling (DRIM) for Recommender Systems. We apply a capsule network in a multi-interest extractor to generate multiple user interest vectors. Each interest of the user should have a certain degree of distinction, thus we introduce three strategies as the diversity regularized separator to separate multiple user interest vectors. Experimental results on public and industrial data sets demonstrate the ability of the model to capture different interests of a user and the superior performance of the proposed approach.