Research on recommendation system based on implicit rating
基于隐式评分的推荐系统研究

Recommendation system based on implicit rating was proposed to improve the precision and solve the problems of "scarcity" and "cold-start". Firstly, this research set up the items' profiles, and adopted the BP neural network to analyze the guiding model and behavior model of the users, gave the forecast rating for the hit items and set up subjective evaluation model and the profiles of preference for the users. Then it forecasted the rating of the non-hit items and formedthe intense rating matrix of user forcast item. After that, it produced the effective recommendation through the adoption of collaborative filtering recommendation algorithm. Finally, the model of negotiation and strategy based on item's characteristics were brought out for recommendation result, which can explain the result and support the bargaining of both sides.