Abstract: The traditional recommendation algorithm,represented by collaborative filtering,can provide users with a high recommended list with high accuracy,while ignoring another important measure which is diversity in the recommendation system.With the increasing development of social networks,with a lot of redundancy and duplication of information,the overload information makes it more difficult to find user interests quickly and effectively.For recommending the most content for users to meet their hobbies,user interests with a significant relevance and covering different aspects are needed.Therefore,based on social relations and user preferences,this paper proposed a sorting framework for diversity and relevance.Firstly,this paper introduced the social relations graph model,considering the relationship between users and items to better model their relevance.Then,this paper used a linear model to integrate the two important indexes of diversity and relevance.Finally,the algorithm was implemented by Spark GraphX parallel graph calculation framework,and experiments were carried on real dataset to verify the feasibility and scalability of the proposed algorithm.

Key words: Diversity, Personalized recommendation system, Relevance, Social network, Spark GraphX