一种预填补社团聚类的兴趣点推荐算法
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摘要
基于位置的社交网络(LBSN)中的数据信息往往会存在数据稀疏,甚至部分信息缺失的情况,导致推荐的准确性不高.兴趣点推荐系统中蕴含着丰富的多源异构数据,如好友关系数据、地理位置数据以及用户对兴趣点的评分等,使用这些数据可以有效提升兴趣点推荐算法的准确率.本文提出一种预填补社团聚类的兴趣点推荐算法.通过社团聚类算法来分别对签到评分数据以及好友关系数据建模得到用户对兴趣点的个人兴趣和社交兴趣,并添加距离影响因素.从而建立了SoGS模型来进行兴趣点推荐.并且提出一种基于相容类的预填补算法来缓解原始用户-兴趣点评分矩阵的稀疏性问题,融合SoGS模型进行对比实验.实验采用Yelp数据集,结果表明,SoGS模型能有效提高兴趣点推荐系统的准确率和召回率.
The location-based social networks( LBSN) often have sparse data,even with some information missing,resulting in lowaccuracy of the recommendation. Points of interest( POI) recommendation system contains a wealth of heterogeneous data,such as friends relationship data,geographic data and POI's score,making use of these data can effectively improve the accuracy of the POI recommendation algorithm. this paper proposes a prefilling and community clustering based POI recommendation approach. Through community clustering algorithm,we build user interest data and sign-in score data to get users' interest and social interest in POIs,and add distance influence factors. we give a joint model SoGS( Social,Geographical and Score) to make the recommendation by fusing the data of social relationships,geographic location data and user-poi-rating matrix. Moreover,a prefilling algorithm based on compatible classes( PACC) is proposed to alleviate the sparsity problem of the original user-poi-rating matrix,and then fuse the SoGS model for comparative experiments. A contrast experiment is performed on the Yelp dataset. The result shows that,our model which adds PACC can improve the precision and recall rate.
The location-based social networks( LBSN) often have sparse data,even with some information missing,resulting in lowaccuracy of the recommendation. Points of interest( POI) recommendation system contains a wealth of heterogeneous data,such as friends relationship data,geographic data and POI's score,making use of these data can effectively improve the accuracy of the POI recommendation algorithm. this paper proposes a prefilling and community clustering based POI recommendation approach. Through community clustering algorithm,we build user interest data and sign-in score data to get users' interest and social interest in POIs,and add distance influence factors. we give a joint model SoGS( Social,Geographical and Score) to make the recommendation by fusing the data of social relationships,geographic location data and user-poi-rating matrix. Moreover,a prefilling algorithm based on compatible classes( PACC) is proposed to alleviate the sparsity problem of the original user-poi-rating matrix,and then fuse the SoGS model for comparative experiments. A contrast experiment is performed on the Yelp dataset. The result shows that,our model which adds PACC can improve the precision and recall rate.
引文
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[2]Bao Jie,Zheng Yu,Wilkie D,et al.Recommendations in location-based social netw orks:a survey[J].Geoinformatica,2015,19(3):525-565.
[3]Yu Gang,Wang Zhi-yan,Shao Lu,et al.Personalized comment recommendation based on singular value decomposition[J].Journal of University Electronic Science and Technology of China,2015,44(4):605-610.
[4]Ye Mao,Yin Pei-feng,Lee W C.Location recommendation for location-based social netw orks[C].Proceedings of the 18th SIGSpatial International Conference on Advances in Geographic Information Systems,ACM,2010:458-461.
[5]Jamali M,Ester M.A matrix factorization technique with trust propagation for recommendation in social netw orks[C].ACMConference on Recommender Systems,ACM,2010:135-142.
[6]Ahmed A,Hong L,Smola A J.Hierarchical geographical modeling of user locations from social media posts[C].Internat-ional Conference on World Wide Web,ACM,2013:25-36.
[7]Ference G,Ye M,Lee W C.Location recommendation for out-oftow n users in location-based social netw orks[C].Procee-dings of the 22nd ACM International Conference on Information&Know ledge M anagement,ACM,2013:721-726.
[8]Wang Guo-yin,Yao Yi-yu,Yu Hong.A survey on rough set theory and applications[J].Chinese Journal of Computers,2009,32(7):1229-1246.
[9]Wang Guo-yin.Extension of rough set under incomplete information systems[J].Journal of Computer Research&Development,2002,39(10):1238-1243.
[10]Ye Mao,Yin Pei-feng,Lee W C,et al.Exploiting geographical influence for collaborative point-of-interest recommendation[C].Proceeding of the International ACM SIGIR Conference on Research and Development in Information Retrieval,Beijing,China,2011:325-334.
[11]Glassman C C N R.Location-based services:foursquare and gow alla,should libraries play?[J].Journal of Electronic Resources in M edical Libraries,2010,7(4):336-343.
[12]Liu Shu-dong,Meng Xiang-wu.Position based social network recommendation system[J].Journal of Computer,2015,38(2):322-336.
[13]Feng Bin,Tan Jian-jun,Li Shao-rong,et al.Design and implementation of marine geological survey database management system[J].Computer Engineering,2009,35(3):29-31.
[14]Ren Xing-yi,Song Mei-na,Song Jun-de.Point-of-interest recommendation based on the user check in behavior[J].Chinese Journal of Computers,2017,40(1):28-51.
[15]Salakhutdinov R,Mnih A.Probabilistic matrix factorization[C].International Conference on Neural Information Processing Systems,Curran Associates Inc,2007:1257-1264.
[16]Lee D D,Seung H S.Learning the parts of objects by non-negative matrix factorization[J].Nature,1999,401(6755):788-791.
[3]余刚,王知衍,邵璐,等.基于奇异值分解的个性化评论推荐[J].电子科技大学学报,2015,44(4):605-610.
[8]王国胤,姚一豫,于洪.粗糙集理论与应用研究综述[J].计算机学报,2009,32(7):1229-1246.
[9]王国胤.Rough集理论在不完备信息系统中的扩充[J].计算机研究与发展,2002,39(10):1238-1243.
[12]刘树栋,孟祥武.基于位置的社会化网络推荐系统[J].计算机学报,2015,38(2):322-336.
[13]冯斌,谭建军,李绍荣,等.海洋地质调查数据库管理系统设计与实现[J].计算机工程,2009,35(3):29-31.
[14]任星怡,宋美娜,宋俊德.基于用户签到行为的兴趣点推荐[J].计算机学报,2017,40(1):28-51.