Location context aware collective filtering algorithm

Abstract

To improve the quality of the recommendation of the recommendation system, a distance-interest affective model is proposed to combine user location context on the preferences of user interests. Based on the model and user-based collaborative filtering algorithm, the location context aware collective filtering algorithm is designed. Firstly, measure the location-similarity between users through the user's location context information. Second, calculate the origin user-similarity from the user-item rating matrix. Then, gain the location-similarity as a weight of final user similarity, calculate the final similarity. Finally, recommendation is supplied by top-N recommendation. The simulation results were compared with the traditional algorithm to prove the precision and recall rate of the proposed algorithm is superior to traditional algorithms. © 2013 Springer-Verlag.