Understanding the Effectiveness of Reviews in E-commerce Top-N Recommendation

TL;DR

This paper investigates how textual reviews influence top-N recommendation in E-commerce, finding that reviews can improve implicit-feedback models when used as regularizers, but optimal methods remain uncertain.

Contribution

It adapts state-of-the-art review-based rating models for top-N recommendation and compares their performance and efficiency, highlighting the potential and limitations of review information.

Findings

Review-only models underperform compared to implicit-feedback matrix factorization.

Using reviews as regularizers improves recommendation performance.

Optimal review-utilization strategies for top-N recommendation are still unresolved.

Abstract

Modern E-commerce websites contain heterogeneous sources of information, such as numerical ratings, textual reviews and images. These information can be utilized to assist recommendation. Through textual reviews, a user explicitly express her affinity towards the item. Previous researchers found that by using the information extracted from these reviews, we can better profile the users' explicit preferences as well as the item features, leading to the improvement of recommendation performance. However, most of the previous algorithms were only utilizing the review information for explicit-feedback problem i.e. rating prediction, and when it comes to implicit-feedback ranking problem such as top-N recommendation, the usage of review information has not been fully explored. Seeing this gap, in this work, we investigate the effectiveness of textual review information for top-N recommendation under E-commerce settings. We adapt several SOTA review-based rating prediction models for top-N recommendation tasks and compare them to existing top-N recommendation models from both performance and efficiency. We find that models utilizing only review information can not achieve better performances than vanilla implicit-feedback matrix factorization method. When utilizing review information as a regularizer or auxiliary information, the performance of implicit-feedback matrix factorization method can be further improved. However, the optimal model structure to utilize textual reviews for E-commerce top-N recommendation is yet to be determined.