Improved Recurrent Neural Networks for Session-based Recommendations

2016/06/27 by Yong Tan, Yong Kiam Tan, Tan, Yong Kiam +4 · 65 citations
Computer Science · Mathematics · Psychology · #Artificial intelligence #Artificial neural network #Cognitive psychology #Computer science #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare #Machine learning #Mathematics #Psychology #Rank (graph theory) #Recall #Reciprocal #Recommender Systems and Techniques #Recurrent neural network #Session (web analytics) #Task (project management) #Topic Modeling #cs.LG

paper · pdf · doi:10.48550/arxiv.1606.08117

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2016/06/27 · arxiv created 2016/09/16 · arxiv updated 2016/09/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08

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

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