Reinforcement Learning Algorithm Based Hybrid Filtering Image Recommender System
Yan Shen, Hak-Chul Shin, Dae-Gi Kim, Yo-Hoon Hong, Phill-Kyu Rhee · 한국인터넷방송통신학회 논문지 · 2012
With the advance of internet technology and fast growing of data volume, it become very hard to find a demanding information from the huge amount of data. Recommender system can solve the delema by helping a user to find required information. This paper proposes a reinforcement learning based hybrid recommendation system to predict user`s preference. The hybrid recommendation system combines the content based filtering and collaborate filtering, and the system was tested using 2000 images. We used mean abstract error(MAE) to compare the performance of the collaborative filtering, the content based filtering, the naive hybrid filtering, and the reinforcement learning algorithm based hybrid filtering methods. The experiment result shows that the performance of the proposed hybrid filtering performance based on reinforcement learning is superior to other methods.