Converged Recommendation System Based on RNN and BP Neural Networks

摘要

Recommendation systems based on rating prediction model didn't consider temporal context and recurrent pattern. And past user behavior analysis didn't simultaneously consider long-term behavior, short-term behavior, and behavior with popular elements. In this paper, we propose a new model RNN-BPNNCM to predict user's next consumption behavior and then we recommend predicted item to the user. First, we use recurrent neural networks to analyze three kinds of behavior sequences to figure out three kinds of probabilities for every possible item. Then we use back propagation neural networks to figure out final probability. Finally, we take the top items with highest final probabilities as items that user will most likely consume next time and we recommend them to the user. RNN-BPNNCM solves the above problems of temporal context, recurrent pattern, and multiple user behaviors. Experiment shows RNN-BPNNCM more accurately predict the user's next consumption behavior and has more excellent performance for recommendation systems.