基于预测的LBSN兴趣点推荐算法

Prediction Based on Point-of-Interest Recommendation Algorithm in LBSN

兴趣点(Point-Of-Interest,POI)推荐是基于位置的社交网络中(Location-Based Social Networks,LBSN)一种重要的个性化推荐功能.本文提出基于预测的兴趣点推荐算法.该算法根据LBSN中用户历史POI数据分布学习用户出行行为,利用变阶的马尔科夫算法根据当前位置预测用户未来到达POI的语义信息,最终推荐时考虑用户签到次数的差异为用户推荐N个具有高兴趣度的POI.实验结果表明:本文提出的算法在准确率和召回率上均高于两个对比算法,说明该算法提高了兴趣点推荐效果,并可以有效的推荐给用户下一个访问的兴趣点.

Point-Of-Interest(POI)recommendation is an important personalized recommendation function in Location-Based Social Networks(LBSN).This paper proposes a prediction point based recommendation algorithm.The algorithm learns the user’s travel behavior according to the user history POI data distribution in the LBSN,and uses the variable Markov algorithm to predict the semantic information of the user’s future arrival POI according to the current location.The final recommendation takes into account the difference in the number of user sign-ups and recommends Nfor the user.Highly interesting POI.The experimental results show that the proposed algorithm is higher than the two comparison algorithms in accuracy and recall rate,which indicates that the algorithm improves the recommendation effect of interest points and can effectively recommend the next interest points for users.