Jungkyu Han
ABSTRACT
Human mobility analysis with Location-Based Social Network (LBSN) data is the basis of personalized point-of-interest (POI) recommendations or location-aware advertisements. In addition to personal preference and spatiotemporal factors such as time and distance, personal context has a strong influence on mobility. An individual's familiarity with an area is an interesting context because it can bias the influence of certain factors. For example, the mobility patterns of two persons who have similar preferences are different when their familiarity with the area is different, even in the same area. In this paper, we analyze familiarity's effect on mobility patterns by using over 1.4 million check-ins gathered from Foursquare. The analysis indicates that there is a skewness of the visit time and visited venue distribution in unfamiliar areas. For instance, people go to unfamiliar areas on weekends; and venues for cultural experiences, such as museums, strongly contribute to the motivation of visit.
- Z. Cheng, J. Caverlee, K. Lee, D. Z. Sui. 2011. Exploring Millions of Footprints in Location Sharing Services, In Proc. of the 5th Int'l Conference on Weblogs and Social Media (July 2011). AAAI'11.Google Scholar
- E. Cho, S. A. Myers, J. Leskovec. 2011. Friendship and mobility: user movement in location-based social networks. In Proc. of the 17th ACM SIGKDD Int'l Conf. on Knowledge discovery and data mining, 1082--1090. KDD'11.Google ScholarDigital Library
- K. Joseph, C. H. Tan, K. M. Carley. 2012. Beyond "Local", "Categories", and "Friends":Clustering foursquare Users with Latent "Topics". In Proc. of the 2012 ACM Conf. on Ubiquitous Computing, 919--926. UrbComp'12.Google ScholarDigital Library
- Y. Qu, J. Zhang. 2013. Trade Area Analysis using User Generated Mobile Location Data. In Proc. of the 22nd Int'l Conf. on World Wide Web, 1053--1064. WWW'13.Google ScholarDigital Library
- P. Georgiev, A. Noulas, C. Mascolo. 2014 Where Business Thrive: Predicting the Impact of the Olympic Games on Local Retailers through Location-Based Service Data, In Proc. of the 8th Int'l Conference on Weblogs and Social Media. AAAI'14Google Scholar
- J. Cranshaw, R. Schwartz, J. Hong, N. Sadeh. 2012. The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City, In Proc. of the 6th Int'l AAAI Conf. on Weblogs and Social Media.Google Scholar
- T. H. Silva, P. O. S. V. Melo, J. Almeida, M. Musolesi, A. Loureiro, 2014. You are What you Eat (and Drink): Identifying Cultural Boundaries by Analyzing Food & Drink Habits in Foursquare, In Proc. of the 8th Int'l Conference on Weblogs and Social Media. AAAI'14Google Scholar
- M. Ye, P. Yin, W. Lee, D. Lee. 2011. Exploiting geographical influence for collaborative point-of-interest recommendation. In Proc. of the 34th Int'l ACM SIGIR Conf. on Research and development in Information Retrieval, 325--334. SIGIR'11.Google ScholarDigital Library
- C. Cheng, H. Yang, I. King, M. R. Lyu. 2012. "Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks", In Proc. of 26th Int'l Conference on Artificial Intelligence. AAAI'12Google Scholar
- J. Zhang, C. Chow. 2013. iGSLR: Personalized Geo-Social Location Recommendation -- A Kernel Density Estimation Approach. In Proc. of the 21st ACM SIGSPATIAL Int'l Conference on Advances in Geographic Information Systems, 334--343. SIGSPATIAL'13.Google ScholarDigital Library
- T. Kurashima, T. Iwata, T. Hoshide, N. Takaya and K. Fujimura. Geo Topic Model: Joint Modeling of User's Activity Area and Interests for Location Recommendation. In Proc. of the 6th ACM Int'l conference on Web search and data mining, 375--384. WSDM'13.Google Scholar
- H. Gao, J. Tang, X. Hu, H. Liu. 2013. Exploring Temporal Effects for Location Recommendation on Location-Based Social Networks. In Proc. of the 7th ACM conference on Recommender systems, 93--100. RecSys'13.Google ScholarDigital Library
- Q. Yuan, G. Cong, Z. Ma, A. Sun, N. Magnenat-Thalmann, 2013. Time-aware Point-of-interest Recommendation. In Proc. of the 36th Int'l ACM SIGIR Conference on Research and development in Information Retrieval, 363--372. SIGIR'13.Google ScholarDigital Library
- C. Cheng, H. Y., M. R. Lyu, I. King. 2013. Where You Like to Go Next: Successive Point-of-Interest Recommendation. In Proc. of the 23th Int'l joint Conference on Artificial Intelligence, 2605--2611. IJCAI'13.Google Scholar
- X. Liu, Y. Liu, K. Aberer, C. Miao. 2013. Personalized Point-of-Interest Recommendation by Mining User's Preference Transition. In Proc. of the 22nd ACM Int'l Conf. on information & knowledge management, 728--733, CIKM'13.Google ScholarDigital Library
- J. Bao, Y. Zheng, M. F. Mokbel. 2012. Location-based and Preference-Aware Recommendation Using Sparse Geo-Social Networking Data. In Proc. of ACM SIGSPATIAL. GIS'12.Google ScholarDigital Library
- H. Yin, Y. Sun, B. Cui, Z. Hu, L. Chen. 2013. LCARS: a location-content-aware recommender system. In Proc. of the 19th ACM SIGKDD Int'l Conference on Knowledge discovery and data mining, 221--229. KDD'13.Google ScholarDigital Library
- D. Lian, C. Zhao, X. Xie, G. Sun, E. Chen, Y. Rui. 2014, GeoMF: Joint Geographical Modeling and Matrix Factorization for Point-of-Interest Recommendation. In Proc. of the 20th ACM SIGKDD Int'l Conference on Knowledge discovery and data mining, 831--840. KDD'14.Google ScholarDigital Library
- H. Gao, J. Tang and H. Liu. 2014. Addressing the cold-start problem in location recommendation using geo-social correlations. Journal of Data Mining and Knowledge Discovery, (Jan. 2014). DMKD'14Google Scholar
- L. Baltrunas, Berend Ludwig, S. Peer, F. Ricci. 2012. Context relevance assessment and exploitation in mobile recommender systems. Personal and Ubiquitous Computing archive, Vol. 16, Issue 5(June. 2012), 507--526.Google Scholar
- J. Sander, M. Ester, H. P. Kriegel, X. Xu. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. of the 2nd ACM SIGKDD Int'l Conf. on Knowledge discovery and data mining, KDD'96.Google Scholar
- K. Pearson. 1895. Notes on regression and inheritance in the case of two parents, In Proc. of the Royal Society of London, Vol 58. 240--242Google ScholarCross Ref
- J. Eisenstein, A. Ahmed, E. P. Xing. 2011. Sparse Additive Generative Models of Text, In Proc. of the 28th Int'l Conf. on Machine Learning, ICML'11.Google Scholar
- Q. Mei, X. Ling, M. Wondra, H. Su, and C. X. Zhai. 2007. Topic sentiment mixture: modeling facets and opinions in weblogs. In Proc. of the 16th Int'l Conf. on World Wide Web, 171--180, WWW'07.Google Scholar
- B. Liu, Y. Fu, Z. Yao, H. Xiong. 2013. Learning Geographical Preferences for Point-of-Interest Recommendation. In Proc. of the 19th ACM SIGKDD Int'l Conf. on Knowledge discovery and data mining, 1043--1051. KDD'13Google ScholarDigital Library
- D. Yang, D. Zhang, Z. Yu, Z. Wang. 2013. A Sentiment-Enhanced Personalized Location Recommendation System. In Proc. of 24th ACM Conf. on Hypertext and Social Media, 119--128, HT'13.Google ScholarDigital Library
Index Terms
- Why people go to unfamiliar areas?: analysis of mobility pattern based on users' familiarity
Recommendations
Geographic Diversification of Recommended POIs in Frequently Visited Areas
In the personalized Point-Of-Interest (POI) (or venue) recommendation, the diversity of recommended POIs is an important aspect. Diversity is especially important when POIs are recommended in the target users’ frequently visited areas, because users are ...
POI Recommendation Algorithm for Mobile Social Network Based on User Perference Tracking
CONF-CDS 2021: The 2nd International Conference on Computing and Data SciencePoint of Interest (POI) recommendation is to use the user check-in data in location-based Social Networks (LBSN) to predict the next Location that the user will visit. With the rapid development of LBSN, users' check-in information (rating information, ...
A POI Recommendation Model for Intelligent Systems Using AT-LSTM in Location-Based Social Network Big Data
In location-based social networks (LBSN), users can check-in at points of interest (POI) to record their trips. POI recommendation is an important service provided by LBSN; it can help users quickly find POI of interest, and also help POI providers more ...
Comments