Abstract: Users have different interests in different regions,and when recommended items are spatial,users tend to travel a limited distance when visiting these venues.Accurately capturing user preferences according to the users’and items’ location can improve the precision in recommender systems.To effectively deal with users’and items’ location information,this paper introduced Pyramid Model(PM) in recommender systems for realizing users’ partitioning and calculating travel penalty,and presented a collaborative filtering recommendation algorithm based on Pyramid model(PMCF) to generate Top-N recommend.MovieLens,Foursquare and Synthetic data set were quoted to evaluate the effectiveness of the algorithm.Experimental results show our algorithm has significant improvements in terms of effectiveness measured through precision.

Key words: Location-aware,Pyramid model,Collaborative filtering,Recommender systems