Constructing the Core User Set for Collaborative Recommendation Based on Samples Selection Idea

As an effective recommendation technology to solve "information overload" problem, collaborative filtering has widly attracted attention of scholars from various fields.First, this paper proposes a method for measuring the recommending ability of user based on popularity and long-tailed distribution.Then, a global core user set is constructed for recommadition based on the recommending ability and samples selection idea in data mining, aimming to take advantage of users in the different part of the long tail distribution and reduce computing complexity of the algorithm without lowing the recommended performance.Experimental results show that the algorithm is effective and can be used to solve the real-time problem and cold start recommendation.