Improved Recurrent Neural Networks for Session-based Recommendations

2016/06/27 by Yong Tan, Xinxing Xu, Tan, Yong Kiam +3 · 15 citations
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare #Recommender Systems and Techniques #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1606.08117

openalex publication_date 2016/06/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Distilling the Knowledge in a Neural Network
• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Return of the Devil in the Details: Delving Deep into Convolutional Nets
• Matrix Factorization Techniques for Recommender Systems
• Long Short-Term Memory
• Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
• Session-based Recommendations with Recurrent Neural Networks
• Deep Residual Learning for Image Recognition

Cited by

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• Generative Sequential Recommendation via Hierarchical Behavior Modeling
• KGBridge: Knowledge-Guided Prompt Learning for Non-overlapping Cross-Domain Recommendation
• ORBIT -- Open Recommendation Benchmark for Reproducible Research with Hidden Tests
• Integrating Sequential and Relational Modeling for User Events: Datasets and Prediction Tasks
• Towards a Real-World Aligned Benchmark for Unlearning in Recommender Systems
• Collaborative local–global context modeling for session-based recommendation
• DenseRec: Revisiting Dense Content Embeddings for Sequential Transformer-based Recommendation
• Temporal User Profiling with LLMs: Balancing Short-Term and Long-Term Preferences for Recommendations
• Integrating LLM-Derived Multi-Semantic Intent into Graph Model for Session-based Recommendation
• Reinforce Lifelong Interaction Value of User-Author Pairs for Large-Scale Recommendation Systems
• A collaborative filtering model with heterogeneous neural networks for recommender systems
• Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based Recommendation

Related

• Session-based Recommendations with Recurrent Neural Networks
• Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell Time
• Inter-Session Modeling for Session-Based Recommendation
• Augmenting Recurrent Neural Networks with High-Order User-Contextual Preference for Session-Based Recommendation
• Time is of the Essence: a Joint Hierarchical RNN and Point Process Model for Time and Item Predictions