Papers › GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation
GHRS: Graph-based Hybrid Recommendation System with Application to Movie Recommendation
Zahra Zamanzadeh Darban, Mohammad Hadi Valipour
Research about recommender systems emerges over the last decade and comprises valuable services to increase different companies' revenue. Several approaches exist in handling paper recommender systems. While most existing recommender systems rely either on a content-based approach or a collaborative approach, there are hybrid approaches that can improve recommendation accuracy using a combination of both approaches. Even though many algorithms are proposed using such methods, it is still necessary for further improvement. In this paper, we propose a recommender system method using a graph-based model associated with the similarity of users' ratings, in combination with users' demographic and location information. By utilizing the advantages of Autoencoder feature extraction, we extract new features based on all combined attributes. Using the new set of features for clustering users, our proposed approach (GHRS) has gained a significant improvement, which dominates other methods' performance in the cold-start problem. The experimental results on the MovieLens dataset show that the proposed algorithm outperforms many existing recommendation algorithms on recommendation accuracy.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Movie Recommendation | MovieLens 1M | GHRS | RMSE | 0.833 | #5 of 5 | Archive leaderboard | report |
| Recommendation Systems | MovieLens 100K | GHRS | RMSE (u1 Splits) | 0.887 | #2 of 18 | Archive leaderboard | report |
| Recommendation Systems | MovieLens 1M | GHRS | Precision | 0.792 | #8 of 31 | Archive leaderboard | report |
| Recommendation Systems | MovieLens 1M | GHRS | RMSE | 0.838 | #8 of 31 | 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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