Review-based Recommender Systems: A Survey of Approaches, Challenges and Future Perspectives

TL;DR

This survey reviews recent developments in review-based recommender systems, emphasizing their importance, challenges, and future research directions, including multimodal data integration and ethical considerations.

Contribution

It provides a comprehensive categorization and analysis of state-of-the-art review-based recommender systems, highlighting their features, effectiveness, and limitations.

Findings

Review-based systems improve recommendation interpretability.

Integration of textual reviews enhances personalization.

Future directions include multimodal data and ethical issues.

Abstract

Recommender systems play a pivotal role in helping users navigate an overwhelming selection of products and services. On online platforms, users have the opportunity to share feedback in various modes, including numerical ratings, textual reviews, and likes/dislikes. Traditional recommendation systems rely on users explicit ratings or implicit interactions (e.g. likes, clicks, shares, saves) to learn user preferences and item characteristics. Beyond these numerical ratings, textual reviews provide insights into users fine-grained preferences and item features. Analyzing these reviews is crucial for enhancing the performance and interpretability of personalized recommendation results. In recent years, review-based recommender systems have emerged as a significant sub-field in this domain. In this paper, we provide a comprehensive overview of the developments in review-based recommender systems over recent years, highlighting the importance of reviews in recommender systems, as well as the challenges associated with extracting features from reviews and integrating them into ratings. Specifically, we present a categorization of these systems and summarize the state-of-the-art methods, analyzing their unique features, effectiveness, and limitations. Finally, we propose potential directions for future research, including the integration of multimodal data, multi-criteria rating information, and ethical considerations.