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

arXiv: https://arxiv.org/abs/2405.05562 · HTML: https://mlsanity.com/paper/2405.05562/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2024.

## Built on by (most cited first, 8 of 8)

- [A Survey of Foundation Model-Powered Recommender Systems: From Feature-Based, Generative to Agentic Paradigms](https://mlsanity.com/paper/2504.16420/) (2025)
- [RLHF Fine-Tuning of LLMs for Alignment with Implicit User Feedback in Conversational Recommenders](https://mlsanity.com/paper/2508.05289/) (2025)
- [XAI-enhanced Comparative Opinion Mining via Aspect-based Scoring and Semantic Reasoning](https://mlsanity.com/paper/2603.01212/) (2026)
- [Toward User Preference Alignment in LLM Recommendation via Explicit Context Feedback](https://mlsanity.com/paper/2605.29141/) (2026)
- [Interaction-Grounded Learning for Contextual Markov Decision Processes with Personalized Feedback](https://mlsanity.com/paper/2602.08307/) (2026)
- [Sensory-Aware Sequential Recommendation via Review-Distilled Representations](https://mlsanity.com/paper/2603.02709/) (2026)
- [How Much Do Reviews Really Contribute? A Study on Text-Enriched Matrix Factorization for Recommendations](https://mlsanity.com/paper/2606.16973/) (2026)
- [Scaling Articulated Rationales for MLLM-based Recommendation](https://mlsanity.com/paper/2609.17639/) (2026)

## Abstract (arXiv)

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.