Improved Recurrent Neural Networks for Session-based Recommendations

, , and . Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, page 17--22. New York, NY, USA, ACM, (2016)


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.

Links and resources

BibTeX key:
search on:

Comments and Reviews  

There is no review or comment yet. You can write one!


Cite this publication