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

2024-05-09

arXiv.org

AI Summary

This paper addresses core challenges in leveraging user reviews for recommendation systems—namely, inadequate review text modeling, weak integration with explicit ratings, and insufficient interpretability—and proposes the first unified taxonomy for review-enhanced recommendation, systematically surveying representative works from 2014 to 2024. Methodologically, it integrates BERT/LSTM encoders, graph neural networks, attention mechanisms, and multi-task learning to jointly model review semantics, user-item interactions, and fine-grained features (e.g., attribute-level preferences). Its contributions are threefold: (1) it formally defines three emerging research frontiers—multimodal fusion, multi-criteria rating modeling, and ethics-aligned recommendation; (2) it identifies critical bottlenecks in model generalizability, robustness to sparse reviews, and attribution-based interpretability; and (3) it outlines theoretically grounded yet practically feasible future research directions.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Surveying review-based recommender systems approaches and challenges
• Analyzing textual reviews to enhance recommendation performance
• Exploring future directions like multimodal data integration

Innovation

• Utilizing textual reviews for fine-grained preferences
• Categorizing review-based recommender systems
• Integrating multimodal data for future research