位置社交网络中谱嵌入增强的兴趣点推荐算法

doi:10.11959/j.issn.1000-436x.2020053

Abstract

Abstract:

In order to effectively capture the spatial characteristics of multi-dimensional context information in LBSN,and deeply explore the non-linear interaction between users and POIs,a spectral embedding enhanced POI recommendation algorithm,namely PSC-SMLP,was proposed.A preference enhanced spectral clustering algorithm (PSC) and a novel spectral embedded enhanced neural network (SMLP) was designed to solve the above problems.Compared with state-of-the-art algorithms on two datasets,PSC-SMLP has better performance in terms of the precision,recall,nDCG and mean average precision.

Key words: location-based social network, POI recommendation, spectral clustering, spectral embedding, neural network

CLC Number:

TP391

Zhen LIU,Na’na WANG,Xiaodong WANG,Yongqi SUN. Spectral clustering and embedding-enhanced POI recommendation in location-based social network[J]. Journal on Communications, 2020, 41(3): 197-206.

Figures/Tables 12

References 23

[1]	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.
	LIU S D , MENG X W . Recommender systems in location-based social networks[J]. Journal of Computers, 2015,38(2): 322-336.
[2]	蒋仲安, 王明, 陈雅 . 基于地理坐标和轨迹数据的路径推荐方法[J]. 通信学报, 2017,38(5): 165-171.
	JIANG Z A , WANG M , CHEN Y . Path recommendation based on geographic coordinates and trajectory data[J]. Journal of Communications, 2017,38(5): 165-171.
[3]	MOHAMMAD A , CRESTANI F . Personalized context-aware point of interest recommendation[J]. ACM Transactions on Information Systems, 2018,36(4):28.
[4]	LI X , GAO C , LI XL ,et al. Rank-GeoFM:a ranking based geographical factorization method for point of interest recommendation[C]// ACM Special Interest Group on International Retrieval. New York:ACM Press, 2015: 433-442.
[5]	LIAN D , ZHENG K , GE Y ,et al. GeoMF++:scalable location recommendation via joint geographical modeling and matrix factorization[J]. ACM Transactions on Information Systems, 2018,36(3): 1-29.
[6]	FAN W , LI Q , CHENG M . Deep modeling of social relations for recommendation[C]// AAAI Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2018: 8075-8076.
[7]	CHEN C , ZHANG M , WANG C ,et al. An efficient adaptive transfer neural network for social-aware recommendation[C]// ACM Special Interest Group on International Retrieval. New York:ACM Press, 2019: 225-234.
[8]	CHENG C , YANG H , KING I ,et al. Fused matrix factorization with geographical and social influence in location-based social networks[C]// AAAI Conference on Artificial Intelligence. Palo Alto:AAAI Press, 2012: 1-8.
[9]	ZHENG N , JIN X , LI L . Cross-region collaborative filtering for new point-of-interest recommendation[C]// Proceedings of the 22nd International Conference on World Wide Web. New York:ACM Press, 2013: 45-46.
[10]	MA C , ZHANG Y , WANG Q ,et al. Point-of-Interest recommendation:exploiting self-attentive auto encoders with neighbor-aware influence[C]// Proceedings of the 27th ACM International Conference on Information and Knowledge Management. New York:ACM Press, 2018: 697-706.
[11]	ZHAO Z , YANG Q , LU H ,et al. Learning max-margin geosocial multimedia network representations for point-of-interest suggestion[C]// Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM Press, 2017: 833-836.
[12]	YANG C , BAI L , ZHANG C ,et al. Bridging collaborative filtering and semi-supervised learning:a neural approach for POI recommendation[C]// Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM Press, 2017: 1245-1254.
[13]	YING H , CHEN L , XIONG Y ,et al. PGRank:personalized geographical ranking for point-of-interest recommendation[C]// Proceedings of the 25th International Conference Companion on World Wide Web. New York:ACM Press, 2016: 137-138.
[14]	HE X , LIAO L , ZHANG H ,et al. Neural collaborative filtering[C]// Proceedings of the 26th International Conference Companion on World Wide Web. New York:ACM Press, 2017: 173-182
[15]	ZHENG L , CHUN T L , JIANG F ,et al. Spectral collaborative filtering[C]// Proceedings of the 12th ACM Conference on Recommender Systems. New York:ACM Press, 2018: 311-319.
[16]	XIE M , YIN H , WANG H ,et al. Learning graph-based POI embedding for location-based recommendation[C]// Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. New York:ACM Press, 2016: 15-24.
[17]	ZHANG J D , CHOW C Y . iGSLR:personalized geo-social location recommendation:a kernel density estimation approach[C]// Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York:ACM Press, 2013: 334-343.
[18]	WANG H , TERROVITIS M , MAMOULIS N . Location recommendation in location-based social networks using user check-in data[C]// Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York:ACM Press, 2013: 374-383.
[19]	ZHANG J D , CHOW C Y , LI Y . Lore:exploiting sequential influence for location recommendations[C]// Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York:ACM Press, 2014: 103-112.
[20]	YE M , YIN P , 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. New York:ACM Press, 2011: 325-334.
[21]	LIU Y , WEI W , SUN A ,et al. Exploiting geographical neighborhood characteristics for location recommendation[C]// Proceedings of the 23rd ACM International Conference on Information and Knowledge Management. New York:ACM Press, 2014: 739-748
[22]	JONATHAN L H , JOSEPH A K , LOREN G T ,et al. Evaluating collaborative filtering recommender systems[J]. ACM Transactions on Information Systems, 2004,22(1): 5-53.
[23]	TANG J , HU X , GAO H ,et al. Exploiting local and global social context for recommendation[C]// Proceedings of the 23rd International Joint Conference on Artificial Intelligence. New York:ACM Press, 2013: 2712-2718.

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用户类别	平均共同签到数量/个
好友之间	3.977
非好友之间	0.517

数据集	用户数/个	POI数/个	签到数/次	稀疏度	用户平均签到数量/次	POI平均被签到数量/个
Gowalla	18 737	32 510	1 278 274	0.997 90	68	40
Yelp	30 887	18 995	860 888	0.998 53	27	45

神经元个数/个			accuracy
神经元个数/个	h=1	h=2	h=3	h=4	h=5
4	0.48	0.612 2	0.731 7	0.843	0.871
8	0.620 4	0.782 9	0.825	0.852	0.878
16	0.744 0	0.841 3	0.852 1	0.865	0.903
32	0.874 9	0.891 4	0.901	0.910	0.915
64	0.892 0	0.912	0.921	0.935	0.93

Spectral clustering and embedding-enhanced POI recommendation in location-based social network

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