针对用户-兴趣点矩阵稀疏以及难于从隐反馈中获取用户对未访问位置的偏好而影响兴趣点推荐准确度的问题，本文提出了一种融合社交地理位置信息的加权矩阵分解兴趣点推荐算法（SGWMF）。首先，通过用户之间的相关性对社交信息进行幂律分布建模，基于用户好友的签到信息计算用户访问位置概率；其次，利用地理信息符合幂律分布特点重构用户访问位置偏好矩阵，缓解矩阵数据稀疏性问题；再次，为了增强加权矩阵分解模型的有效性，通过建模社交信息和地理信息挖掘出用户对未访问位置的偏好，并以隐反馈项的形式改进加权矩阵分解的目标函数；最后，在两个真实数据集上对算法性能进行对比验证，结果表明本文算法的性能要优于其他兴趣点推荐算法，推荐结果的准确性有明显提高。

The point-of-interest （POI） recommendation services provided by the location-based social network （LBSN） have become an important means of mining users' preference for POIs. The sparsity of user-POI matrix is the primary problem to be solved， and a large number of unknown values in implicit feedback cannot reflect user preferences. To improve recommendation precision， this paper proposes a point of interest recommendation algorithm integrating social geographical information based on weighted matrix factorization （SGWMF）. The social information is modeled through the power-law distribution. The check-in information of the user's friends is converted into the user's visit location preference. Secondly， the power-law distribution of geographical information is used to construct the user's visit location preference matrix to alleviate the data sparsity problem. Thirdly， in order to extend the effectiveness of the model， we improve the objective function by adding implicit feedback term. Finally， the experimental results on two datasets show that it has better performance than other POI recommendation algorithms and can improve the accuracy of recommendation results.