Group Interests and Their Correlations Mining Based on Wikipedia
Zhang Hai · Chinese Journal of Computers · 2011
Personalized recommendation technologies,such as collaborative filtering and content based filtering,face some problems.The obvious ones are the privacy history data collection and cold start.In this paper,we suggest a group interests mining method from Wikipedia.We also apply the group interests into the recommendation system,which avoid the cold start,and don't need any privacy data.Here,the group interest replaces the personalized interest in the traditional personalized recommendation technologies.In detail,we first suggest a general tree structure and a growing strategy to denote the interest of a users group,which includes the semantic relationship of each interest.Then we define the group interest based on the structure of users groups.At last,we measure the correlations of interests according to the general tree structure of interests.We further design three types of experiment to evaluate the reasonability of group interests,which is manual evaluation,test set evaluation and a news recommendation experiment in video service.The results show that,the accuracy of correlation between group interests can be more than 50%,and the news hits rate on the recommendation from group interests is 2 times larger than that on the recommendation from news popularity.