Improving RNN Based Recommendation by Embedding-Weight Tying

Abstract

Many researchers recently paid attention to applying deep learning to collaborative recommendation. Especially, RNN(Recurrent Neural Network)-based recommender system was shown to learn users' interest and preference from temporal sequences of users' movie consumption records, and they could make better recommendation compared to conventional collaborative recommendation. In this work, we present an embedding-weight tying approach to RNN-based recommendation in order to improve the performance of movie recommender system more. In many cases, our approach outperforms existing RNN-based recommendation as well as currently popular collaborative recommendation in terms of short-term prediction success(sps) and recall.