Improved user similarity computation for finding friends in your location

Abstract

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.

A Point-of-Interest Recommendation Algorithm Combining Social Influence and Geographic Location Based on Belief Propagation

“…There are many ways to calculate user similarity. In this paper, the cosine similarity is used to calculate the user similarity in the friend set [21]. For users u and user v, the similarity calculation is as follows:…”