Papers › Interpretable Recommender System With Heterogeneous Information: A Geometric Deep...
Interpretable Recommender System With Heterogeneous Information: A Geometric Deep Learning Perspective
Yan Leng, Rodrigo Ruiz, Xiaowen Dong, Alex Pentland
Recommender systems (RS) are ubiquitous in the digital space. This paper develops a deep learning-based approach to address three practical challenges in RS: complex structures of high-dimensional data, noise in relational information, and the black-box nature of machine learning algorithms. Our method—Multi-Graph Graph Attention Network (MG-GAT)—learns latent user and business representations by aggregating a diverse set of information from neighbors of each user (business) on a neighbor importance graph. MG-GAT out-performs state-of-the-art deep learning models in the recommendation task using two large-scale datasets collected from Yelp and four other standard datasets in RS. The improved performance highlights MG-GAT’s advantage in incorporating multi-modal features in a principled manner. The features importance, neighbor importance graph and latent representations reveal business insights on predictive features and explainable characteristics of business and users. Moreover, the learned neighbor importance graph can be used in a variety of management applications, such as targeting customers, promoting new businesses, and designing information acquisition strategies. Our paper presents a quintessential big data application of deep learning models in management while providing interpretability essential for real-world decision-making.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Recommendation Systems | Douban Monti | MG-GAT | RMSE | 0.727 | #3 of 8 | Archive leaderboard | report |
| Recommendation Systems | Flixster Monti | MG-GAT | RMSE | 0.876 | #2 of 7 | Archive leaderboard | report |
| Recommendation Systems | MovieLens 100K | MG-GAT | RMSE (u1 Splits) | 0.890 | #4 of 18 | Archive leaderboard | report |
| Recommendation Systems | YahooMusic Monti | MG-GAT | RMSE | 18.9 | #1 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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