Group recommendation algorithm for online community users.

By combining advantages of the most two prevalent group recommending methods, this paper built a new algorithm frame and introduced the disagreement factor to perfect the model. In addition, considering the specialty of online groups, it defined a variable to describe the interaction frequency among group members, and evaluated its effect on recommending results by analyzing its relationship with the recommending precision index. It used the data of Douban to test the efficacy of this algorithm. Results show that the algorithm considering disagreement factor obtains better recommendation effect, and that a well-designed interaction mechanism contributes to improving recommending precision. [ABSTRACT FROM AUTHOR]