LBSN中融合类别信息的混合推荐模型

针对基于位置的社交网络（Location-Based Social Network，LBSN）中用户签到数据的高稀疏性问题及用户隐私问题，提出了一种混合推荐模型（SoGeoCat）.首先，通过用户潜在兴趣点数据模型，学习用户的潜在兴趣点；其次，将用户的潜在兴趣点纳入融合类别信息的矩阵分解模型中并优化；最后，根据用户特征矩阵、兴趣点特征矩阵，提出推荐策略.基于Foursquare真实数据集，实验结果表明：（1）相比于其他几个推荐模型，该算法将用户的潜在兴趣点填充至用户-兴趣点矩阵中，可以有效地缓解数据稀疏性的影响；（2）该算法可保护用户家庭信息；（3）在推荐模型中纳入类别信息的影响能提高推荐效果.

Hybrid Recommendation Model Integrating Category Information in LBSN

Aiming at the high sparsity problem of user''s check-in data and user privacy in LBSN, a hybrid recommendation model (SoGeoCat) is proposed. Firstly, the user''s potential point-of-interest is learnt from the user potential point of interest data model. Secondly, the user''s potential point-of-interest is incorporated into a category based matrix factorization model and then optimized. Finally, the proposed recommended strategy is according to the user and feature matrix and the point-of-interest matrix. Based on the Foursquare real dataset, the experimental results show that:(1) compared with several other recommended models, the algorithm fills the user''s potential point-of-interest into the matrix, which can effectively alleviate the impact of data sparsity; (2) the algorithm can protect the user''s family information; (3) the influence of the category information in the recommendation model can improve the recommendation effect.