Improved Recurrent Neural Networks for Session-based Recommendations

TL;DR

This paper enhances RNN-based session recommendation models by applying data augmentation, handling data shifts, and exploring distillation and embedding prediction, leading to significant performance improvements on benchmark data.

Contribution

It introduces techniques like data augmentation and data shift handling to improve RNN session recommendation models, along with empirical evaluation of distillation and embedding prediction methods.

Findings

12.8% improvement in Recall@20

14.8% improvement in MRR@20

Effective techniques for data augmentation and distribution shift handling

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.