Location Recommendation Algorithm Based on Spatial-temporal Similarity of User

doi:10.19678/j.issn.1000-3428.0047996

Abstract

Abstract:

In the location recommendation,the result of finding users’ neighborhood is not good by using user-based collaborative filtering because of the sparseness of the check-in data,which results in the poor recommendation.Aiming at this problem,this paper proposes a location recommendation algorithm which considers user’s time and geographical similarity.It integrates the time and geography information of check-in data to compute user’s similarity.Based on the periodic influence of time on the user check-in behavior,the time attribute is introduced by dividing the user-location matrix over time.A time-similarity calculation method is designed,and the user-location-time matrix is filled according to the time similarity,which alleviates the problem of user-location-time sparseness caused by time segmentation.Based on spatial aggregation of user’s check-in behavior,the user spatial similarity is computed by combining the distance between the location and the center of user active region found by multi-center clustering algorithm and users’ preference of active region.Experiments is implemented on Foursquare dataset.Experimental result shows that the proposed algorithm achieves superior precision,recall and F1-measure compared with the traditional user-based location recommendation algorithm.

Key words: location-based social network, location recommendation, collaborative filtering, spatial-temporal similarity, user preference

摘要：

在位置推荐中用户签到数据具有稀疏性的特点,基于用户的协同过滤算法难以准确搜索邻近用户,从而影响推荐效果。针对该问题,分别将用户签到的时间信息与空间信息融入用户相似度计算中,提出考虑用户时空相似性的位置推荐算法。根据时间对用户签到行为的周期性影响,通过对用户签到矩阵按时间进行分割引入时间属性,设计一种时间相似性计算方法,并根据时间相似性对用户-地点-时间矩阵进行填补,缓解因时间分割导致的用户-地点-时间矩阵高稀疏问题。基于用户签到行为的空间聚集性,通过多中心聚类算法发现用户签到的活跃区域,结合用户对活跃区域的偏好以及未签到地点与活跃区域中心的距离,计算用户的空间相似性。在Foursquare数据集上的实验结果表明,与传统基于用户的协同过滤算法相比,该方法在准确率、召回率与F1-Measure评估值方面性能均有所提高。

关键词: 基于位置的社交网络, 位置推荐, 协同过滤, 时空相似性, 用户偏好

CLC Number:

TP18

JIANG Cuiqing,SHU Deyou,DUAN Rui. Location Recommendation Algorithm Based on Spatial-temporal Similarity of User[J]. Computer Engineering, 2018, 44(7): 177-182.

蒋翠清,疏得友,段锐. 基于用户时空相似性的位置推荐算法[J]. 计算机工程, 2018, 44(7): 177-182.

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References

［1］CHORLEY M J,WHITAKER R M,ALLEN S M.Personality and location-based social networks［J］.Computers in Human Behavior,2015,46:45-56.
［2］SCELLATO S,NOULAS A,LAMBIOTTE R,et al.Socio-spatial properties of online location-based social networks［C］//Proceedings of the 5th International Conference on Weblogs and Social Media.Barcelona,Spain:AAAI,2012:329-336.
［3］HOROZOV T,NARASIMHAN N,VASUDEVAN V.Using location for personalized POI recommendations in mobile environments［C］//Proceedings of International IEEE Symposium on Applications and the Internet.Washington D.C.,USA:IEEE Press,2006:124-129.
［4］CHENG C,YANG H,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:AAAI,2012:48.
［5］YE M,YIN P,LEE W C.Location recommendation for location-based social networks［C］//Proceedings of ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems.New York,USA:ACM Press,2010:458-461.
［6］SCHAFER J B,DAN F,HERLOCKER J,et al.Collaborative filtering recommender systems［J］.ACM Transactions on Information Systems,2004,22(1):45-53.
［7］JIANG S,QIAN X,SHEN J,et al.Author topic model-based collaborative filtering for personalized POI recommendations［J］.IEEE Transactions on Multimedia,2015,17(6):907-918.
［8］DAO T H,JEONG S R,AHN H.A novel recommendation model of location-based advertising:context-aware collaborative filtering using GA approach［J］.Expert Systems with Applications,2012,39(3):3731-3739.
［9］任看看,钱雪忠.协同过滤算法中的用户相似性度量方法研究［J］.计算机工程,2015,41(8):18-22.
［10］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.
［11］WANG W,ZHANG G,LU J.Collaborative filtering with entropy-driven user similarity in recommender systems［M］.［S.l.］:John Wiley & Sons,Inc.,2015.
［12］YUAN Q,CONG G,MA Z,et al.Time-aware point-of-interest recommendation［C］//Proceedings of International ACM SIGIR Conference on Research and Development in Information Retrieval.New York,USA:ACM Press,2013:363-372.
［13］GAO H,TANG J,LIU H.gSCorr:modeling geo-social correlations for new check-ins on location-based social networks［C］//Proceedings of ACM International Conference on Information and Knowledge Management.New York,USA:ACM Press,2012:1582-1586.
［14］YE M,YIN P,LEE W C,et al.Exploiting geographical influence for collaborative point-of-interest recommenda-tion［C］//Proceedings of International ACM SIGIR Conference on Research and Development in Information Retrieval.New York,USA:ACM Press,2011:325-334.
［15］YANG D,ZHANG D,ZHENG V W,et al.Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs［J］.IEEE Transactions on Systems Man and Cybernetics Systems,2014,45(1):129-142.

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