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Review-based Recommender Systems: A Survey of Approaches, Challenges and Future Perspectives
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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.
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Cited by 6 Pith papers
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ELIXIR: Efficient and LIghtweight model for eXplaIning Recommendations
ELIXIR combines aspect-aware user and item representations with prompt tuning on a frozen 60M-parameter T5 to generate personalized reviews, outperforming GPT-2-based baselines on TripAdvisor and RateBeer.
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RecUserSim: A Realistic and Diverse User Simulator for Evaluating Conversational Recommender Systems
RecUserSim combines profile, memory, action, and refinement modules in an LLM agent to generate realistic, diverse user utterances and multi-dimensional ratings for evaluating conversational recommender systems.
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Learning to Shop Like Humans: A Review-driven Retrieval-Augmented Recommendation Framework with LLMs
RevBrowse retrieves preference-relevant pros and cons from reviews via a contrastively trained module, then uses an LLM to rerank candidates; experiments on four Amazon datasets show consistent improvements over baselines.
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Reason4Rec: Deliberative User Preference Alignment of Large Language Models for Recommendation
An LLM recommender that decomposes preference reasoning into summarization, matching, and prediction steps, trained on review text, improves rating accuracy and explanation quality on Amazon and Yelp benchmarks.
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A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms
A survey that organizes foundation-model recommender systems into feature-based, generative, and agentic paradigms and reviews tasks, empirical results, and open challenges.
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Sentiment-Aware Recommendation Systems in E-Commerce: A Review from a Natural Language Processing Perspective
A 2023-2025 survey of sentiment-aware e-commerce recommenders, arguing that review-text sentiment improves accuracy and explainability over rating-only models.
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