Recently, the study of social network‐based recommender systems has become an active research topic. The integration of the social relationships that exist between users can improve the accuracy of recommendation results since the users' preferences are similar or influenced by their connected friends. We focus in this article on the recommendation of users in social networks. Our approach is based on semantic and social representations of the users' profiles. We have formalized and illustrated these two dimensions using the Yelp social network. The novelty of our approach concerns the modelling of the credibility of the user, through his/her trust and commitment in the social network. Moreover, in order to optimize the performance of the recommendation process, we have used two classification techniques: an unsupervised technique that uses the K‐means algorithm (applied initially to all users); and a supervised technique that uses the K‐Nearest Neighbours algorithm (applied to newly added users). A recommendation algorithm has been proposed taking into account the cold‐start and sparsity problems. A prototype of a recommender system has been developed and tested using two publicly available datasets: the Yelp database and the Rich Epinions database. The comparative evaluation results show the effectiveness of combining the semantic, the social and the credibility information in an approach that appropriately uses the K‐means and K‐Nearest Neighbours algorithms.

中文翻译：

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推荐社交网络中的用户：一种基于语义和社交的分类方法

最近，基于社交网络的推荐系统的研究已成为一个活跃的研究主题。用户之间存在的社交关系的整合可以提高推荐结果的准确性，因为用户的偏好是相似的，或者受他们所连接的朋友影响。我们在本文中重点关注社交网络中用户的推荐。我们的方法基于用户个人资料的语义和社交表示。我们已经使用Yelp社交网络对这两个维度进行了形式化和说明。我们方法的新颖性在于通过用户对社交网络的信任和承诺来对用户信誉进行建模。此外，为了优化推荐过程的性能，我们使用了两种分类技术：一种使用K均值算法的无监督技术（最初应用于所有用户）；以及使用K最近邻居算法的监督技术（适用于新添加的用户）。考虑到冷启动和稀疏性问题，提出了一种推荐算法。推荐系统的原型已使用两个公开可用的数据集开发和测试：Yelp数据库和Rich Epinions数据库。比较评估结果表明，在适当使用K-means和K-Nearest Neighbors算法的方法中，将语义，社会和信誉信息相结合的有效性。考虑到冷启动和稀疏性问题，提出了一种推荐算法。推荐系统的原型已使用两个公开可用的数据集开发和测试：Yelp数据库和Rich Epinions数据库。比较评估结果表明，在适当使用K-means和K-Nearest Neighbors算法的方法中，将语义，社会和信誉信息相结合的有效性。考虑到冷启动和稀疏性问题，提出了一种推荐算法。推荐系统的原型已使用两个公开可用的数据集开发和测试：Yelp数据库和Rich Epinions数据库。比较评估结果表明，在适当使用K-means和K-Nearest Neighbors算法的方法中，将语义，社会和信誉信息相结合的有效性。