A Joint Summarization and Pre-Trained Model for Review-Based Recommendation

Yi Bai,Yang Li,Letian Wang

doi:10.3390/info12060223

Abstract

Currently, reviews on the Internet contain abundant information about users and products, and this information is of great value to recommendation systems. As a result, review-based recommendations have begun to show their effectiveness and research value. Due to the accumulation of a large number of reviews, it has become very important to extract useful information from reviews. Automatic summarization can capture important information from a set of documents and present it in the form of a brief summary. Therefore, integrating automatic summarization into recommendation systems is a potential approach for solving this problem. Based on this idea, we propose a joint summarization and pre-trained recommendation model for review-based rate prediction. Through automatic summarization and a pre-trained language model, the overall recommendation model learns a fine-grained summary representation of the key content as well as the relationships between words and sentences in each review. The review summary representations of users and items are finally incorporated into a neural collaborative filtering (CF) framework with interactive attention mechanisms to predict the rating scores. We perform experiments on the Amazon dataset and compare our method with several competitive baselines. Experimental results show that the performance of the proposed model is obviously better than that of the baselines. Relative to the current best results, the average improvements obtained on four sub-datasets randomly selected from the Amazon dataset are approximately 3.29%.

Highlights

With the increasing abundance of products, research on high-quality recommendation systems, especially for the task of rate prediction, has become very important for online e-commerce platforms and users
We propose modeling user reviews via a Joint Summarization and Pre-Trained Recommendation model (JSPTRec) for the task of rate prediction
We empirically evaluate the various components of our proposed JSPTRec model for rate prediction

Summary

Introduction

With the increasing abundance of products, research on high-quality recommendation systems, especially for the task of rate prediction, has become very important for online e-commerce platforms and users. Most early recommendation systems use collaborative filtering (CF), including user-based collaborative filtering and item-based collaborative filtering. CF has its own limitations and drawbacks It has difficulty generating reliable recommendations for users or items with few ratings (the well-known cold-start problem). Another drawback of CF technology is that it does not make full use of the available context information. Context information, such as item attributes [1] or user profiles, is not considered when making recommendations

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Conclusion