Service Recommendation Based on User Dynamic Preference Extraction and Prediction
Yanmei Zhang, Qian Ya, Mengjiao Gan, Xiaoyi Tang, Zheng Lin · 2019
User preferences are dynamically changing due to a variety of factors. Therefore, how to effectively extract the user's current preferences and accurately predict subsequent user preferences is one of the keys to improving service recommendation. This paper proposes a deep learning-based user preference prediction method, which combines user dynamic preference extraction and preference prediction. Firstly, the time-series LDA model is used to extract the user dynamic preference, then the LSTM neural network method is used to predict the user's preference of next time slice based on the user dynamic preference sequence. Finally, services are recommended to users based on predicted preference. The experimental results on real-world dataset show that the prediction performance of Time-aware-gate LSTM is slightly better than traditional LSTM, and the recommendation performance of the recommendation algorithm that takes preference prediction into account is better than those without preference prediction.