Papers › Session-based Recommendations with Recurrent Neural Networks

Session-based Recommendations with Recurrent Neural Networks

21 Nov 2015arXiv:1511.06939archive 2025-07-28

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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Songweiping/GRU4Rec_TensorFlow mentioned on GitHubtf report
UlionTse/mlgb mentioned on GitHubpytorch report
bekleyis95/RNN-RecSys mentioned on GitHubpytorch report
hidasib/GRU4Rec mentioned on GitHubtfNOASSERTION report
hidasib/gru4rec_pytorch_official mentioned on GitHubpytorch report
hu-dske/ILSTP mentioned on GitHubtf report
hungpthanh/gru4rec-pytorch mentioned on GitHubpytorch report
hungthanhpham94/GRU4REC-pytorch mentioned on GitHubpytorch report
ifuseok/TripRecommendation mentioned on GitHubtf report
jacklu2016/kerasGRU4Rec_c mentioned on GitHubtfApache-2.0 report
maciejkula/spotlight mentioned on GitHubpytorchMIT report
massquantity/LibRecommender mentioned on GitHubtfMIT report
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nggianno/thesis mentioned on GitHubtf report
paxcema/KerasGRU4Rec mentioned on GitHubtf report
yeganegi-reza/torch-gru4rec mentioned on GitHubpytorch report
yhs968/pyGRU4REC mentioned on GitHubpytorch report
yoavnavon/GRU4REC-spotify mentioned on GitHubtf report

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augment paxcema/KerasGRU4Rec/preprocess/extractDwellTime.py community (archive-listed) ran · our draft was wrong no licence file found · pointer only · bbfdf27ff0771dd9 · report
item_is_in yoavnavon/GRU4REC-spotify/preprocess.py community (archive-listed) ran · violated contract no licence file found · pointer only · 4793120067f5b394 · report
bpr_loss maciejkula/spotlight/spotlight/losses.py community (archive-listed) unverified MIT (permissive) · 1951fae7bf0d9f85 · report
compute_dwell_time paxcema/KerasGRU4Rec/preprocess/extractDwellTime.py community (archive-listed) unverified no licence file found · pointer only · 6d75a51574deff05 · report
cpu maciejkula/spotlight/spotlight/torch_utils.py community (archive-listed) unverified MIT (permissive) · 3a4a700c8335a075 · report
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mrr_score maciejkula/spotlight/spotlight/evaluation.py community (archive-listed) unverified MIT (permissive) · 11cb70776f03d1bc · report
pointwise_loss maciejkula/spotlight/spotlight/losses.py community (archive-listed) unverified MIT (permissive) · 09deb08fd3fd83ce · report
preprocess_df paxcema/KerasGRU4Rec/preprocess/extractDwellTime.py community (archive-listed) unverified no licence file found · pointer only · 600da17c8e8aa6c4 · report
removeShortSessions hungpthanh/gru4rec-pytorch/preprocessing.py community (archive-listed) unverified Apache-2.0 (permissive) · 46ec454eb67dc4f9 · report
sample_items maciejkula/spotlight/spotlight/sampling.py community (archive-listed) unverified MIT (permissive) · ce350f99f574d036 · report
sequence_mrr_score maciejkula/spotlight/spotlight/evaluation.py community (archive-listed) unverified MIT (permissive) · db5a21db6d916e28 · report
sequence_precision_recall_score maciejkula/spotlight/spotlight/evaluation.py community (archive-listed) unverified MIT (permissive) · fefa69dbc273e191 · report

Tasks

Recommendation SystemsSession-Based Recommendations

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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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