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Improved Recurrent Neural Networks for Session-based Recommendations

arXiv 1606.08117 ↗First posted 27 Jun 2016cs.LGcited by 86

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Abstract (arXiv)

Recurrent neural networks (RNNs) were recently proposed for the session-based recommendation task. The models showed promising improvements over traditional recommendation approaches. In this work, we further study RNN-based models for session-based recommendations. We propose the application of two techniques to improve model performance, namely, data augmentation, and a method to account for shifts in the input data distribution. We also empirically study the use of generalised distillation, and a novel alternative model that directly predicts item embeddings. Experiments on the RecSys Challenge 2015 dataset demonstrate relative improvements of 12.8% and 14.8% over previously reported results on the Recall@20 and Mean Reciprocal Rank@20 metrics respectively.

Key facts. Cited by 86 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Jun 2016.

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Citing papers per year: 2021: 20; 2022: 9; 2023: 12; 2024: 16; 2025: 19; 2026: 10.

86 papers.

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  1. 2021A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendationcited by 59
  2. 2021Attention, please! A survey of Neural Attention Models in Deep Learningcited by 51
  3. 2023TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterestcited by 35
  4. 2021AutoLoss: Automated Loss Function Search in Recommendationscited by 31
  5. 2023Large Language Models for Intent-Driven Session Recommendationscited by 30
  6. 2023A Survey on User Behavior Modeling in Recommender Systemscited by 26
  7. 2021A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Modelscited by 25
  8. 2023AutoMLP: Automated MLP for Sequential Recommendationscited by 25
  9. 2021RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystemscited by 21
  10. 2021Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameterscited by 17
  11. 2022Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendationcited by 11
  12. 2022BagPipe: Accelerating Deep Recommendation Model Trainingcited by 10
38 more
  1. 2023MobileRec: A Large-Scale Dataset for Mobile Apps Recommendationcited by 8
  2. 2024Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendationcited by 8
  3. 2024Unleash LLMs Potential for Sequential Recommendation by Coordinating Dual Dynamic Index Mechanismcited by 8
  4. 2021Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commercecited by 7
  5. 2024Scaling Sequential Recommendation Models with Transformerscited by 7
  6. 2024Molar: Multimodal LLMs with Collaborative Filtering Alignment for Enhanced Sequential Recommendationcited by 7
  7. 2025Universal Item Tokenization for Transferable Generative Recommendationcited by 7
  8. 2021English to Bangla Machine Translation Using Recurrent Neural Networkcited by 6
  9. 2025Towards Explainable Temporal User Profiling with LLMscited by 6
  10. 2022Data Augmentation Strategies for Improving Sequential Recommender Systemscited by 4
  11. 2025Pre-training Generative Recommender with Multi-Identifier Item Tokenizationcited by 4
  12. 2021Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Searchcited by 3
  13. 2021Augmenting the User-Item Graph with Textual Similarity Modelscited by 3
  14. 2021HyperTeNet: Hypergraph and Transformer-based Neural Network for Personalized List Continuationcited by 3
  15. 2021Breaking the Softmax Bottleneck for Sequential Recommender Systems with Dropout and Decouplingcited by 3
  16. 2021Dynamic Review-based Recommenderscited by 3
  17. 2022STAR: A Session-Based Time-Aware Recommender Systemcited by 3
  18. 2023Time-aware Hyperbolic Graph Attention Network for Session-based Recommendationcited by 3
  19. 2023Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start Recommendationcited by 3
  20. 2022G3SR: Global Graph Guided Session-based Recommendationcited by 2
  21. 2022Heterogeneous Information Crossing on Graphs for Session-based Recommender Systemscited by 2
  22. 2024Denoising Time Cycle Modeling for Recommendationcited by 2
  23. 2024Denoising Long- and Short-term Interests for Sequential Recommendationcited by 2
  24. 2024TTT4Rec: A Test-Time Training Approach for Rapid Adaption in Sequential Recommendationcited by 2
  25. 2025Efficient and Responsible Adaptation of Large Language Models for Robust and Equitable Top-k Recommendationscited by 2
  26. 2025SS4Rec: Continuous-Time Sequential Recommendation with State Space Modelscited by 2
  27. 2025Temporal User Profiling with LLMs: Balancing Short-Term and Long-Term Preferences for Recommendationscited by 2
  28. 2025DenseRec: Revisiting Dense Content Embeddings for Sequential Transformer-based Recommendationcited by 2
  29. 2026Rethinking Generative Recommender Tokenizer: Recsys-Native Encoding and Semantic Quantization Beyond LLMscited by 2
  30. 2021Improving Sequential Recommendation with Attribute-augmented Graph Neural Networkscited by 1
  31. 2021Hybrid Model with Time Modeling for Sequential Recommender Systemscited by 1
  32. 2021Learning Complex Users' Preferences for Recommender Systemscited by 1
  33. 2022Graph Spring Network and Informative Anchor Selection for Session-based Recommendationcited by 1
  34. 2023PDT: Pretrained Dual Transformers for Time-aware Bipartite Graphscited by 1
  35. 2024A Temporal Graph Network Framework for Dynamic Recommendationcited by 1
  36. 2024Task Integration Distillation for Object Detectorscited by 1
  37. 2024A Case Study of Next Portfolio Prediction for Mutual Fundscited by 1
  38. 2025Distilling Transitional Pattern to Large Language Models for Multimodal Session-based Recommendationcited by 1

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