The development of social media technologies has greatly enhanced social interactions. The proliferation of social platforms has generated massive amounts of data and a considerable number of persons join these platforms every day. Therefore, one of the current issues is to facilitate the search for the most appropriate friends for a given user. We focus in this article on the recommendation of users in social networks. We propose a novel approach which combines a user‐based collaborative filtering (CF) algorithm with semantic and social recommendations. The semantic dimension suggests the close friends based on the calculation of the similarity between the active user and his friends. The social dimension is based on some social‐behavior metrics such as friendship and credibility degree. The novelty of our approach concerns the modeling of the credibility of the user, through his/her trust and commitment in the social network. A social recommender system based on this approach is developed and experiments have been conducted using the Yelp social network. The evaluation results demonstrated that the proposed hybrid approach improves the accuracy of the recommendation compared with the user‐based CF algorithm and solves the sparsity and cold start problems.

中文翻译：

---

一种基于语义和社交的社交网络好友协作推荐

社交媒体技术的发展极大地增强了社交互动。社交平台的激增产生了大量数据，每天都有相当多的人加入这些平台。因此，当前的问题之一是促进为给定用户搜索最合适的朋友。我们在本文中关注社交网络中用户的推荐。我们提出了一种将基于用户的协同过滤 (CF) 算法与语义和社会推荐相结合的新方法。语义维度基于计算活跃用户与其好友之间的相似度来推荐亲密好友。社会维度基于一些社会行为指标，例如友谊和可信度。我们方法的新颖之处在于通过用户对社交网络的信任和承诺来建模用户的可信度。开发了基于这种方法的社交推荐系统，并使用 Yelp 社交网络进行了实验。评估结果表明，与基于用户的CF算法相比，所提出的混合方法提高了推荐的准确性，并解决了稀疏性和冷启动问题。