Papers › Session-based Recommendations with Recurrent Neural Networks
Session-based Recommendations with Recurrent Neural Networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk
We apply recurrent neural networks (RNN) on a new domain, namely recommender systems. Real-life recommender systems often face the problem of having to base recommendations only on short session-based data (e.g. a small sportsware website) instead of long user histories (as in the case of Netflix). In this situation the frequently praised matrix factorization approaches are not accurate. This problem is usually overcome in practice by resorting to item-to-item recommendations, i.e. recommending similar items. We argue that by modeling the whole session, more accurate recommendations can be provided. We therefore propose an RNN-based approach for session-based recommendations. Our approach also considers practical aspects of the task and introduces several modifications to classic RNNs such as a ranking loss function that make it more viable for this specific problem. Experimental results on two data-sets show marked improvements over widely used approaches.
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Code
Syntology Ran 2 of 15 code samples harvested from 4 repositories linked to this paper; 13 have no recorded run. Of those that ran: 1 ran · violated contract; 1 ran · our draft was wrong.
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Code Syntology ran Syntology
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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 | MovieLens 1M | GRU4Rec | HR@10 (full corpus) | 0.2811 | #31 of 31 | Archive leaderboard | report |
| Recommendation Systems | MovieLens 1M | GRU4Rec | NDCG@10 (full corpus) | 0.1648 | #31 of 31 | Archive leaderboard | report |
| Recommendation Systems | MovieLens 20M | GRU4Rec | HR@10 (full corpus) | 0.2813 | #18 of 18 | Archive leaderboard | report |
| Recommendation Systems | MovieLens 20M | GRU4Rec | nDCG@10 (full corpus) | 0.1730 | #18 of 18 | Archive leaderboard | report |
| Session-Based Recommendations | Diginetica | GRU4REC | Hit@20 | 29.45 | #13 of 13 | Archive leaderboard | report |
| Session-Based Recommendations | Diginetica | GRU4REC | MRR@20 | 8 | #13 of 13 | Archive leaderboard | report |
| Session-Based Recommendations | yoochoose1/64 | GRU4REC | HR@20 | 60.64 | #11 of 11 | Archive leaderboard | report |
| Session-Based Recommendations | yoochoose1/64 | GRU4REC | MRR@20 | 22.89 | #11 of 11 | 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.
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