融合地理信息、种类信息与隐式社交关系的兴趣点推荐算法

针对现有大多数兴趣点推荐算法都存在签到数据稀疏、社交关系难以获取、用户个性难以考虑等问题,文中提出融合地理信息、种类信息与隐式社交关系的兴趣点推荐算法.首先考虑用户签到种类信息,同时分解用户签到地点矩阵和用户签到种类矩阵,减小签到数据稀疏带来的影响.再在显式社交关系的基础上,使用信息熵的方法度量用户的隐式社交关系,缓解社交网络稀疏的问题,并通过正则化的方法在矩阵分解模型中加入该隐式社交关系.最后,使用自适应核密度估计方法个性化建模地理信息对用户签到行为的影响,提高推荐的准确性.在Foursquare、Yelp数据集上的实验验证文中算法的有效性.

Point of Interest Recommendation Algorithm Integrating Geo-Category Information and Implicit Social Relationship

Aiming at the problems in the existing point of interest recommendation algorithms, such as check-in data sparsity, difficulties in obtaining social relation and lack of consideration of user individuality, a point of interest recommendation algorithm integrating geo-category information and implicit social relationship is proposed. Firstly, user check-in category information is considered, and user check-in location matrix and category matrix are decomposed simultaneously to reduce the impact of data sparsity. On the basis of explicit social relations, the method of information entropy is employed to measure user implicit social relations to alleviate the sparse problem of social networks, and then the user implicit social relations are added to the matrix factorization model by regularization method. Finally, the adaptive kernel density estimation method is adopted to personalize the impact of geographic information on user check-in behavior to improve the accuracy of recommendation. Experiments on Foursquare and Yelp datasets verify the effectiveness of the proposed algorithm.