基于社交关系和用户偏好的多样性图推荐方法

以协同过滤为代表的传统推荐算法能够为用户提供准确率较高的推荐列表,但忽略了推荐系统中另外一个重要的衡量标准:多样性。随着社交网络的日益发展,大量冗余和重复的信息充斥其间,信息过载使得快速、有效地发现用户的兴趣爱好变得更加困难。针对某个用户推荐最能满足其兴趣爱好的物品,需要具备显著的相关度且能覆盖用户广泛的兴趣爱好。因此,基于社交关系和用户偏好提出一种面向多样性和相关度的图排序框架。首先,引入社交关系图模型,综合考虑用户及物品之间的关系,以更好地建模它们的相关度；然后,利用线性模型融合多样性和相关性两个重要指标;最后,利用Spark GraphX并行图计算框架实现该算法,并在真实的数据集上通过实验验证所提方法的有效性和扩展性。

Diversity Recommendation Approach Based on Social Relationship and User Preference

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.