Long short-term memory based recurrent neural networks for collaborative filtering
Lixin Zou, Yulong Gu, Jiaxing Song, Weidong Liu, Yuan Yao · 2017
Consuming behaviors of users form sequences ordered by time intuitively. Long Short-Term Memory Based Recurrent Neural Networks(LSTM), which are special kind of Recurrent Neural Networks, are ideal for modeling sequences. In this work, we propose a LSTM based model called CF-LSTM which can model the consuming sequences of users for Collaborative Filtering(CF). To effectively train the CF-LSTM model, we propose the step-combine technique, which processes k ratings at a time step and solves the long sequences problem of ratings. To improve the performance of CF-LSTM, we extend our model with ordinal cost by considering the ordinary nature of users' ratings. Finally, we compare our model with state-of-the-art methods in the metrics of accuracy, novelty and diversity. Extensive experiment results show that CF-LSTM provides highly accurate, novel and diverse recommendations, which outperforms state-of-the-art methods.