A POI Recommendation Algorithm Integrating User's Space Behavior Characteristics
In this paper,a POI recommendation algorithm is proposed in which user's spatial behavior characteris-tics are integrated based on a comprehensive consideration of the characteristics of user's check-in data and density clustering algorithms.Firstly,through the analysis of user's activity ability,the noise points of sign-in data are e-liminated,and the relationship model between user's sign-in tendency and sign-in point spacing is built.Secondly,the KANN-DBSCAN clustering algorithm is used to analyze the user's activity area for different users,and to cap-ture the user's space distribution characteristics.The user's space behavioral characteristics are integrated to rec-ommend Top-N POI.Finally,Gowalla data set is used to compare the proposed algorithm with the other five algo-rithms,and the effectiveness of the algorithm in this paper is verified by two evaluation indicators of the accuracy and recall.Experimental results show that the proposed algorithm effectively improves the quality of POI recommen-dation.
POI recommendationspatial behavior characteristicsclusteringactivity abilityactivity area
万方数据
维普
