Exploration-Exploitation Motivated Variational Auto-Encoder for Recommender Systems

TL;DR

This paper introduces XploVAE, a variational auto-encoder model that balances exploitation of known user preferences and exploration of new items in recommender systems using personalized subgraphs and hierarchical latent spaces.

Contribution

It proposes a novel exploitation-exploration motivated VAE with user-specific subgraphs and hierarchical latent spaces for improved personalized recommendations.

Findings

Effective in leveraging exploitation and exploration tasks

Outperforms existing models on real-world datasets

Enhances recommendation diversity and relevance

Abstract

Recent years have witnessed rapid developments on collaborative filtering techniques for improving the performance of recommender systems due to the growing need of companies to help users discover new and relevant items. However, the majority of existing literature focuses on delivering items which match the user model learned from users' past preferences. A good recommendation model is expected to recommend items that are known to enjoy and items that are novel to try. In this work, we introduce an exploitation-exploration motivated variational auto-encoder (XploVAE) to collaborative filtering. To facilitate personalized recommendations, we construct user-specific subgraphs, which contain the first-order proximity capturing observed user-item interactions for exploitation and the high-order proximity for exploration. A hierarchical latent space model is utilized to learn the personalized item embedding for a given user, along with the population distribution of all user subgraphs. Finally, experimental results on various real-world datasets clearly demonstrate the effectiveness of our proposed model on leveraging the exploitation and exploration recommendation tasks.