Recommender systems are most often used to predict possible ratings that a user would assign to items, in order to find and propose items of possible interest to each user. In our work, we are interested in a system that will analyze user preferences in order to find and connect people with common interests that happen to be in the same geographical area, i.e., a “friend” recommendation system. We present and propose an algorithm, Egosimilar+, which is shown to achieve superior performance against a number of well-known similarity computation methods from the literature. The algorithm adapts ideas and techniques from the recommender systems literature and the skyline queries literature and combines them with our own ideas on the importance and utilization of item popularity.

中文翻译：

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改进了用户相似度计算，可在您的位置找到朋友

推荐系统最常用于预测用户将分配给项目的可能等级，以便查找和建议每个用户可能感兴趣的项目。在我们的工作中，我们对一种系统进行分析，该系统将分析用户的偏好以便发现并联系碰巧在同一地理区域中具有共同兴趣的人，即“朋友”推荐系统。我们提出并提出了一种算法Egosimilar +，它针对文献中的许多众所周知的相似度计算方法均表现出优异的性能。该算法采用了推荐系统文献和天际线查询文献中的思想和技术，并将它们与我们对商品受欢迎程度的重要性和利用的思想相结合。