# Attentive Aspect Modeling for Review-aware Recommendation

arXiv: https://arxiv.org/abs/1811.04375 · HTML: https://mlsanity.com/paper/1811.04375/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2018.

## Built on by (most cited first, 12 of 12)

- [Towards Personalized Answer Generation in E-Commerce via Multi-Perspective Preference Modeling](https://mlsanity.com/paper/2112.13556/) (2021)
- [Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations](https://mlsanity.com/paper/2306.01475/) (2023)
- [Personalized Visualization Recommendation](https://mlsanity.com/paper/2102.06343/) (2021)
- [Knowledge-aware Collaborative Filtering with Pre-trained Language Model for Personalized Review-based Rating Prediction](https://mlsanity.com/paper/2308.02555/) (2023)
- [Learning to Shop Like Humans: A Review-driven Retrieval-Augmented Recommendation Framework with LLMs](https://mlsanity.com/paper/2509.00698/) (2025)
- [HADSF: Aspect Aware Semantic Control for Explainable Recommendation](https://mlsanity.com/paper/2510.26994/) (2025)
- [Rating and aspect-based opinion graph embeddings for explainable recommendations](https://mlsanity.com/paper/2107.03385/) (2021)
- [Attentive Graph-based Text-aware Preference Modeling for Top-N Recommendation](https://mlsanity.com/paper/2305.12976/) (2023)
- [Graphing else matters: exploiting aspect opinions and ratings in explainable graph-based recommendations](https://mlsanity.com/paper/2107.03226/) (2021)
- [Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction](https://mlsanity.com/paper/2109.03821/) (2021)
- [Robust Explainable Recommendation](https://mlsanity.com/paper/2405.01855/) (2024)
- [ELIXIR: Efficient and LIghtweight model for eXplaIning Recommendations](https://mlsanity.com/paper/2508.20312/) (2025)

## Abstract (arXiv)

In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The common aspects mentioned in a user's reviews and a product's reviews indicate indirect connections between the user and product. However, these aspect-based methods suffer from two problems. First, the common aspects are usually very sparse, which is caused by the sparsity of user-product interactions and the diversity of individual users' vocabularies. Second, a user's interests on aspects could be different with respect to different products, which are usually assumed to be static in existing methods. In this paper, we propose an Attentive Aspect-based Recommendation Model (AARM) to tackle these challenges. For the first problem, to enrich the aspect connections between user and product, besides common aspects, AARM also models the interactions between synonymous and similar aspects. For the second problem, a neural attention network which simultaneously considers user, product and aspect information is constructed to capture a user's attention towards aspects when examining different products. Extensive quantitative and qualitative experiments show that AARM can effectively alleviate the two aforementioned problems and significantly outperforms several state-of-the-art recommendation methods on top-N recommendation task.