A Collaborative Filtering Recommendation Based on User Profile and User Behavior in Online Social Networks

Abstract

This paper aims to present and discuss the similarity among users in a social network based on CF (Collaborative Filtering) algorithm and SimRank (Similarity Based on Random Walk) algorithm. The CF algorithm used to predict the relationship between users based on the user rating on items (movies and books) and the user’s profile. The SimRank algorithm calculates the similarity among users through finding the nearest neighbors for each user in the social network. At last, the combination of these two algorithms will be used to get “people may interest each other” from users’ database. In the experimental analysis, a data set “DouBan” (a data set is collected from a Chinese website) will be used and demonstrates the performance of the improved technique with a website. And the website will be developed to show the recommended processing of the proposed algorithm. Finally, the recommendation accuracy of the proposed method is evaluated by comparing with the existing recommendation algorithms.