Improved Recurrent Neural Networks for Session based Recommendations
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.
DLRS@RecSys