# MMALFM: Explainable Recommendation by Leveraging Reviews and Images

arXiv: https://arxiv.org/abs/1811.05318 · HTML: https://mlsanity.com/paper/1811.05318/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2018.

## Built on by (most cited first, 6 of 6)

- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations](https://mlsanity.com/paper/2306.01475/) (2023)
- [Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry](https://mlsanity.com/paper/2111.00905/) (2021)
- [Reliable Shot Identification for Complex Event Detection via Visual-Semantic Embedding](https://mlsanity.com/paper/2110.08063/) (2021)
- [Notion of Explainable Artificial Intelligence -- An Empirical Investigation from A Users Perspective](https://mlsanity.com/paper/2311.02102/) (2023)
- [To Explain Or Not To Explain: An Empirical Investigation Of AI-Based Recommendations On Social Media Platforms](https://mlsanity.com/paper/2508.16610/) (2025)

## Abstract (arXiv)

Although the latent factor model achieves good accuracy in rating prediction, it suffers from many problems including cold-start, non-transparency, and suboptimal results for individual user-item pairs. In this paper, we exploit textual reviews and item images together with ratings to tackle these limitations. Specifically, we first apply a proposed multi-modal aspect-aware topic model (MATM) on text reviews and item images to model users' preferences and items' features from different aspects, and also estimate the aspect importance of a user towards an item. Then the aspect importance is 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 weight matrix to associate those latent factors with the same set of aspects in MATM, 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, every aspect rating is weighted by its aspect importance, which is dependent on the targeted user's preferences and the 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. Comprehensive experimental studies have been conducted on the Yelp 2017 Challenge dataset and Amazon product datasets to demonstrate the effectiveness of our method.