Dynamic Bayesian Contrastive Predictive Coding Model for Personalized Product Search

Abstract

In this paper, we study the problem of dynamic personalized product search. Due to the data-sparsity problem in the real world, existing methods suffer from the challenge of data inefficiency. We address the challenge by proposing a Dynamic Bayesian Contrastive Predictive Coding model (DBCPC), which aims to capture the rich structured information behind search records to improve data efficiency. Our proposed DBCPC utilizes the contrastive predictive learning to jointly learn dynamic embeddings with structure information of entities (i.e., users, products and words). Specifically, our DBCPC employs the structured prediction to tackle the intractability caused by non-linear output space and utilizes the time embedding technique to avoid designing different encoders for each time in the Dynamic Bayesian models. In this way, our model jointly learns the underlying embeddings of entities (i.e., users, products and words) via prediction tasks, which enables the embeddings to focus more on their general attributes and capture the general information during the preference evolution with time. For inferring the dynamic embeddings, we propose an inference algorithm combining the variational objective and the contrastive objectives. Experiments were conducted on an Amazon dataset and the experimental results show that our proposed DBCPC can learn the higher-quality embeddings and outperforms the state-of-the-art non-dynamic and dynamic models for product search.