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计算机系统应用:2019,28(1):200-206
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LBSN中融合类别信息的混合推荐模型
(1.福州大学 经济与管理学院, 福州 350108;2.北京交通大学 下一代互联网互联设备国家工程实验室, 北京 100044)
Hybrid Recommendation Model Integrating Category Information in LBSN
(1.School of Economics and Management, Fuzhou University, Fuzhou 350108, China;2.National Engineering Laboratory for NGI Interconnection Devices, Beijing Jiaotong University, Beijing 100044, China)
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投稿时间:2018-06-30    修订日期:2018-07-27
中文摘要: 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.
Abstract: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.
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基金项目:国家自然科学基金(61300104);福建省自然科学基金(2018J01791)
引用文本:
张岐山,李可,林小榕.LBSN中融合类别信息的混合推荐模型.计算机系统应用,2019,28(1):200-206
ZHANG Qi-Shan,LI Ke,LIN Xiao-Rong.Hybrid Recommendation Model Integrating Category Information in LBSN.COMPUTER SYSTEMS APPLICATIONS,2019,28(1):200-206

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