A Hybrid Movie Recommendation Approach via Social Tags
Shouxian Wei, Litao Xiao, Xiaolin Zheng, Deren Chen · 2014
The social media should be where a large number of netizens contribute, extract, create, and spread news consulting spontaneously, such as the social movie network. It requires frequent operations for users when they face with the vast resources. Personalized recommendation service can effectively solve the problem. However, the accuracy of recommendation service is lower than expected. We put forward a kind of hybrid movie recommendation approach via social tags. According to the user's preference from the social content annotation, e.g. Tags, through a series of the analysis, including the extraction, the normalization and recondition of social tags, we established the mixed recommendation model. Comparing with the existing collaborative filtering algorithms, the experimental results show that the proposed method has increased significantly in recommendation accuracy.