多特征融合的兴趣点推荐算法
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摘要
基于位置社交网络的兴趣点推荐越来越受到工业界和学术界的关注。由于用户签到数据集的稀疏性以及签到地理位置的聚集性,使得目前的推荐算法效率普遍不高,特别是当用户外出到新的地点时,推荐效果更是急剧下降。因此本文提出了一种基于用户-区域-内容主题的多特征联合推荐算法(UCRTM),以隐主题模型为基础,在统一的框架下利用隐含因子关联性融合了用户的偏好、兴趣点的内容以及兴趣点所属地理区域主题等信息来进行推荐,使得用户无论身处何地,都能获得理想的推荐服务。本文在两种真实的数据集上进行了实验,结果表明该方法不仅能够克服数据的稀疏性以及弱语义性等问题,而且与其他方法相比具有更高的推荐准确率。
The point of interest recommendation service is receiving increasing attention from the industry and academia. The sparsity of users' activity history datasets and aggregation of geological position prevent the current recommendation algorithm efficiency from being high, and especially, when a user goes out to a new city, the recommendation effect will fall sharply. Therefore, this paper presents a user-content-region topic model based on a joint recommendation algorithm, considering to the user's preferences, the content of the point of interest, and the geographical area, making users obtain an ideal recommendation service irrespective of their location. An experiment was carried out on two real datasets, and the results show that this method can not only overcome problems such as data sparseness,weak semantic performance, but also has a higher recommendation accuracy compared with other methods.
The point of interest recommendation service is receiving increasing attention from the industry and academia. The sparsity of users' activity history datasets and aggregation of geological position prevent the current recommendation algorithm efficiency from being high, and especially, when a user goes out to a new city, the recommendation effect will fall sharply. Therefore, this paper presents a user-content-region topic model based on a joint recommendation algorithm, considering to the user's preferences, the content of the point of interest, and the geographical area, making users obtain an ideal recommendation service irrespective of their location. An experiment was carried out on two real datasets, and the results show that this method can not only overcome problems such as data sparseness,weak semantic performance, but also has a higher recommendation accuracy compared with other methods.
引文
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[3]ZHAO Yan,ZHU Jia,JIA Mengdi,et al.A novel hybrid friends recommendation framework for twitter[C]//Proceedings of the First International Joint Conference,Web and Big Data.Beijing,China,2017:83-97.
[4]YU Yonghong,WANG Hao,SUN Shuanzhu,et al.Exploiting location significance and user authority for pointof-interest recommendation[C]//Proceedings of the 21st Pacific-Asia Conference,Advances in Knowledge Discovery and Data Mining.Jeju,South Korea,2017:119-130.
[5]INTERDONATO R,INTERDONATO A.Personalized recommendation of points-of-interest based on multilayer local community detection[C]//Proceedings of the 9th International Conference,Social Informatics.Oxford,201 7:552-571.
[6]YU Yonghong,GAO Yang,WANG Hao,et al.Joint user knowledge and matrix factorization for recommender systems[C]//Proceedings of the 1 7th International Conference,Web Information Systems Engineering.Shanghai,China,2016:77-91.
[7]BERJANI B,STRUFE T.A recommendation system for spots in location-based online social networks[C]//Proceedings of the 4th Workshop on Social Network Systems.Salzburg,Austria,201 1:4.
[8]CHENG Chen,YANG Haiqin,KING I,et al.Fused matrix factorization with geographical and social influence in location-based social networks[C]//Proceedings of the 26th AAAI Conference on Artificial Intelligence.Toronto,Canada,2012:17-23.
[9]YE Mao,YIN Peifeng,LEE W C.Location recommendation for location-based social networks[C]//Proceedings of the 1 8th SIGSPATIAL International Conference on Advances in Geographic Information Systems.San Jose,USA,2010:458-461.
[10]FERENCE G,YE Mao,LEE W C.Location recommendation for out-of-town users in location-based social networks[C]//Proceedings of the 22nd ACM International Conference on Information and Knowledge Management.San Francisco,USA,2013:721-726.
[11]YE Mao,YIN Peifeng,LEE W C,et al.Exploiting geographical influence for collaborative point-of-interest recommendation[C]//Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval.Beijing,China,2011:325-334.
[12]YIN Hongzhi,CUI Bin,SUN Yizhou,et al.LCARS:A spatial item recommender system[J].ACM Transactions on Information Systems,2014,32(3):11.
[13]YIN Hongzhi,CUI Bin,Zhou Xiaofang,et al.Joint modeling of user check-in behaviors for real-time point-of-interest recommendation[J].ACM Transactions on Information Systems,2016,35(2):11.
[14]YIN Hongzhi,ZHOU Xiaofang,CUI Bin,et al.Adapting to user interest drift for poi recommendation[J].IEEE Transactions on Knowledge and Data Engineering,2016,28(10):2566-2581.
[15]CREMONESI P,KOREN Y,TURRIN R.Performance of recommender algorithms on top-n recommendation tasks[C]//Proceedings of the 4th ACM Conference on Recommender Systems.Barcelona,Spain,2010:39-46.
[16]BAO Jie,ZHENG Yu,MOKBEL M F.Location-based and preference-aware recommendation using sparse geosocial networking data[C]//Proceedings of the 20th International Conference on Advances in Geographic Information Systems.Redondo Beach,USA,2012:199-208.
[17]LINDEN G,SMITH B,YORK J.Amazon,com recommendations:item-to-item collaborative filtering[J].IEEE Internet Computing,2003,7(1):76-80.