Collaborative Filtering Recommender Algorithm Based on Comments and Score
Yuanqing Zhu, Wei Song, Lizhen Liu, Xinlei Shelly Zhao, Chao Du · 2017
Accompanied by rapid development of Internet technology, people are increasingly dependent on the network. In the past, people is usually passive to accept information, but today people begin to take the initiative to create information. This case makes network data more and more. In order to ease information overload caused by inconvenience, recommender system has gradually been people's attention. Through the appropriate recommended technology, it can help people to filter out useless content, reduce the amount of information faced by individuals. In the recommender system, collaborative filtering recommendation is a more widely used method. In order to improve the recommended results, this paper based on the traditional method of rating. Using user's comments, emotional analysis tools to extract the emotional polarity of comments, combine emotional polarity and rating to enhance final recommended effect.