Affiliation:
1. Sun Yat-sen University, China, and University College London, United Kingdom
2. University of Glasgow, United Kingdom
3. Sun Yat-sen University, China and Mohamed bin Zayed University of Artificial Intelligence, United Arab Emirates
Abstract
In this article, 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 contrastive predictive learning to jointly learn dynamic embeddings with structure information of entities (i.e., users, products, and words). Specifically, our DBCPC employs structured prediction to tackle the intractability caused by non-linear output space and utilizes the time embedding technique to avoid designing different encoders 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.
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications
Reference74 articles.
1. A survey on data‐efficient algorithms in big data era
2. Qingyao Ai, Daniel N. Hill, S. V. N. Vishwanathan, and W. Bruce Croft. 2019. A zero attention model for personalized product search. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 379–388.
3. Qingyao Ai, Yongfeng Zhang, Keping Bi, Xu Chen, and W. Bruce Croft. 2017. Learning a hierarchical embedding model for personalized product search. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 645–654.
4. Explainable product search with a dynamic relation embedding model;Ai Qingyao;ACM Transactions on Information Systems (TOIS),2019
5. Gökhan BakIr, Thomas Hofmann, Alexander J. Smola, Bernhard Schölkopf, and Ben Taskar. 2007. Predicting Structured Data.