# Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews

arXiv: https://arxiv.org/abs/1802.07938 · HTML: https://mlsanity.com/paper/1802.07938/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2018.

## Built on by (most cited first, 19 of 19)

- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [ASAP: A Chinese Review Dataset Towards Aspect Category Sentiment Analysis and Rating Prediction](https://mlsanity.com/paper/2103.06605/) (2021)
- [Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations](https://mlsanity.com/paper/2306.01475/) (2023)
- [Doubly Calibrated Estimator for Recommendation on Data Missing Not At Random](https://mlsanity.com/paper/2403.00817/) (2024)
- [Enhancing Content-based Recommendation via Large Language Model](https://mlsanity.com/paper/2404.00236/) (2024)
- [A Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation](https://mlsanity.com/paper/2403.03600/) (2024)
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- [Quaternion Factorization Machines: A Lightweight Solution to Intricate Feature Interaction Modelling](https://mlsanity.com/paper/2104.01716/) (2021)
- [A Comprehensive Overview of Recommender System and Sentiment Analysis](https://mlsanity.com/paper/2109.08794/) (2021)
- [Aspect-driven User Preference and News Representation Learning for News Recommendation](https://mlsanity.com/paper/2110.05792/) (2021)
- [Towards Explainable Collaborative Filtering with Taste Clusters Learning](https://mlsanity.com/paper/2304.13937/) (2023)
- [HADSF: Aspect Aware Semantic Control for Explainable Recommendation](https://mlsanity.com/paper/2510.26994/) (2025)
- [RMBRec: Robust Multi-Behavior Recommendation towards Target Behaviors](https://mlsanity.com/paper/2601.08705/) (2026)
- [Hierarchical Aspect-guided Explanation Generation for Explainable Recommendation](https://mlsanity.com/paper/2110.10358/) (2021)
- [From Stars to Insights: Exploration and Implementation of Unified Sentiment Analysis with Distant Supervision](https://mlsanity.com/paper/2305.01710/) (2023)
- [Enhancing Topic Extraction in Recommender Systems with Entropy Regularization](https://mlsanity.com/paper/2306.07403/) (2023)
- [Semantic-Enhanced Relational Metric Learning for Recommender Systems](https://mlsanity.com/paper/2406.10246/) (2024)
- [ELIXIR: Efficient and LIghtweight model for eXplaIning Recommendations](https://mlsanity.com/paper/2508.20312/) (2025)
- [Aspect-Aware MOOC Recommendation in a Heterogeneous Network](https://mlsanity.com/paper/2602.05297/) (2026)

## Abstract (arXiv)

Although latent factor models (e.g., matrix factorization) achieve good accuracy in rating prediction, they suffer from several problems including cold-start, non-transparency, and suboptimal recommendation for local users or items. In this paper, we employ textual review information with ratings to tackle these limitations. Firstly, we apply a proposed aspect-aware topic model (ATM) on the review text to model user preferences and item features from different aspects, and estimate the aspect importance of a user towards an item. The aspect importance is then integrated into a novel aspect-aware latent factor model (ALFM), which learns user's and item's latent factors based on ratings. In particular, ALFM introduces a weighted matrix to associate those latent factors with the same set of aspects discovered by ATM, such that the latent factors could be used to estimate aspect ratings. Finally, the overall rating is computed via a linear combination of the aspect ratings, which are weighted by the corresponding aspect importance. To this end, our model could alleviate the data sparsity problem and gain good interpretability for recommendation. Besides, an aspect rating is weighted by an aspect importance, which is dependent on the targeted user's preferences and targeted item's features. Therefore, it is expected that the proposed method can model a user's preferences on an item more accurately for each user-item pair locally. Comprehensive experimental studies have been conducted on 19 datasets from Amazon and Yelp 2017 Challenge dataset. Results show that our method achieves significant improvement compared with strong baseline methods, especially for users with only few ratings. Moreover, our model could interpret the recommendation results in depth.