Abstract: In order to improve the diversity of the collaborative filtering recommender system of videos,this paper proposed a diverse videos collaborative filtering recommender algorithm based on multi-property aggregate.According to the history of interaction between users and recommendation system,users are judged whether they are satisfied with the recommendation items of the system.If a user watches the videos on the same topic produced by different video authors,it indicates that this user shows high diversity to the video authors,and low diversity to the video subjects.Information entropy and user profile length are used to evaluate the diversity of each item’s attributes.According to the combination of the two indicators,the user’s diversity of each item’s attributes is divided into four quadrants,and the user’s diversity is fuzzified to obtain the membership degree of user’s diversity to the four quadrants.In the first phase,it predicts the rates of unrated items.In the second phase,it re-ranks all items,which improves the diversity of recommendation list.At last,experimental results based on the public Movielens 1M dataset show that,the proposed algorithm can realize the similar accuracy with top-N algorithm,at the same time,it enhances the diversity effectively.In the application scenario of balancing recommendation accuracy and diversity,setting appreciate parameters can improve the individual diversity,total diversity and freshness significantly with acceptable recommendation accuracy reduction

Key words: Collaborative filtering recommender algorithm, Diversity enhancement, Electronic commerce, Long tail distribution, Re-ranking algorithm, Video recommender system