Semantic Gaussian Mixture Variational Autoencoder for Sequential Recommendation

TL;DR

This paper introduces SIGMA, a novel VAE-based sequential recommendation model that employs a Gaussian mixture prior to better capture multiple user interests, improving recommendation accuracy.

Contribution

The paper proposes a Gaussian mixture prior in VAE for sequential recommendation, enabling multi-interest modeling and enhancing recommendation performance.

Findings

SIGMA outperforms existing models on public datasets.

The Gaussian mixture prior effectively captures multiple user interests.

The multi-interest-aware ELBO improves sequence representation learning.

Abstract

Variational AutoEncoder (VAE) for Sequential Recommendation (SR), which learns a continuous distribution for each user-item interaction sequence rather than a determinate embedding, is robust against data deficiency and achieves significant performance. However, existing VAE-based SR models assume a unimodal Gaussian distribution as the prior distribution of sequence representations, leading to restricted capability to capture complex user interests and limiting recommendation performance when users have more than one interest. Due to that it is common for users to have multiple disparate interests, we argue that it is more reasonable to establish a multimodal prior distribution in SR scenarios instead of a unimodal one. Therefore, in this paper, we propose a novel VAE-based SR model named SIGMA. SIGMA assumes that the prior of sequence representation conforms to a Gaussian mixture distribution, where each component of the distribution semantically corresponds to one of multiple interests. For multi-interest elicitation, SIGMA includes a probabilistic multi-interest extraction module that learns a unimodal Gaussian distribution for each interest according to implicit item hyper-categories. Additionally, to incorporate the multimodal interests into sequence representation learning, SIGMA constructs a multi-interest-aware ELBO, which is compatible with the Gaussian mixture prior. Extensive experiments on public datasets demonstrate the effectiveness of SIGMA. The code is available at https://github.com/libeibei95/SIGMA.