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.
@article{arxiv.1606.08117,
title = {Improved Recurrent Neural Networks for Session-based Recommendations},
author = {Yong Kiam Tan and Xinxing Xu and Yong Liu},
journal= {arXiv preprint arXiv:1606.08117},
year = {2016}
}