Abstract:

The existing location recommendation algorithms based on collaborative filtering have many problems, such as difficulty in estimating users preference and low recommendation accuracy. In order to improve the traditional similarity calculation algorithms for users, we propose a location recommendation algorithm. We firstly calculate user similarity and location similarity separately, and at the same time, conduct  cross adjustment to the two values continuously till convergence is achieved. The proposed algorithm is more effective for sparse data. In addition, we also take into account the user interest and the distance of the recommended location, and set a threshold to control weights of the two factors so as to adaptively generate recommendation lists. Experimental results show that compared with others, our algorithm can achieve better recommendation results.

Key words: locationbased social networks; location recommendation;collaborative filtering