Friend Recommendation Algorithm Based on Interest Classification with Time Decay

Abstract

Friend recommendation algorithm plays an important role in social networks. However, traditional recommendation algorithms do not take into account the drifting of user interests, and there are also some deficiencies when the recommendation's timeliness is considered. Aimed at this problem, the measure method of similarity was improved by combining with the characteristics of user interest's change with time. A time decay model was introduced to measure the predictive value. At the same time, the method refines the preference of the target users according to the homogeneity theory through the interest preference of friends. Through experiments on Sina microblog data set, and the results show that the algorithm to achieve better recommendation effect on precision, recall and F value.