Defu Lian
Xing Xie
Enhong Chen
Abstract
With the growing popularity of location-based social networks, numerous location visiting records (e.g., check-ins) continue to accumulate over time. The more these records are collected, the better we can understand users’ mobility patterns and the more accurately we can predict their future locations. However, due to the personality trait of neophilia, people also show propensities of novelty seeking in human mobility, that is, exploring unvisited but tailored locations for them to visit. As such, the existing prediction algorithms, mainly relying on regular mobility patterns, face severe challenges because such behavior is beyond the reach of regularity. As a matter of fact, the prediction of this behavior not only relies on the forecast of novelty-seeking tendency but also depends on how to determine unvisited candidate locations. To this end, we put forward a Collaborative Exploration and Periodically Returning model (CEPR), based on a novel problem, Exploration Prediction (EP), which forecasts whether people will seek unvisited locations to visit, in the following. When people are predicted to do exploration, a state-of-the-art recommendation algorithm, armed with collaborative social knowledge and assisted by geographical influence, will be applied for seeking the suitable candidates; otherwise, a traditional prediction algorithm, incorporating both regularity and the Markov model, will be put into use for figuring out the most possible locations to visit. We then perform case studies on check-ins and evaluate them on two large-scale check-in datasets with 6M and 36M records, respectively. The evaluation results show that EP achieves a roughly 20% classification error rate on both datasets, greatly outperforming the baselines, and that CEPR improves performances by as much as 30% compared to the traditional location prediction algorithms.
- D. Ashbrook and T. Starner. 2002. Learning significant locations and predicting user movement with GPS. In Proceedings of ISWC’02. IEEE, 101--108. Google Scholar
Digital Library
- M. Azizyan, I. Constandache, and R. R. Choudhury. 2009. SurroundSense: Mobile phone localization via ambience fingerprinting. In Proceedings of MobiComp’09. ACM, 261--272. Google ScholarDigital Library
- J. L. Bentley and H. A. Maurer. 1980. Efficient worst-case data structures for range searching. Acta Informatica 13, 2 (1980), 155--168. Google ScholarDigital Library
- C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. 2005. Learning to rank using gradient descent. In Proceedings of ICML’05. ACM, 89--96. Google ScholarDigital Library
- J. Chang and E. Sun. 2011. Location3: How users share and respond to location-based data on social. In Proceedings of ICWSM’11.Google Scholar
- S. F. Chen and J. Goodman. 1996. An empirical study of smoothing techniques for language modeling. In Proceedings of ACL’96. ACL, 310--318. Google ScholarDigital Library
- C. Cheng, H. Yang, I. King, and M. R. Lyu. 2012. Fused matrix factorization with geographical and social influence in location-based social networks. In Proceedings of AAAI’12.Google ScholarDigital Library
- Z. Cheng, J. Caverlee, K. Lee, and D. Z. Sui. 2011. Exploring millions of footprints in location sharing services. In Proceedings of ICWSM’11.Google Scholar
- E. Cho, S. A. Myers, and J. Leskovec. 2011. Friendship and mobility: User movement in location-based social networks. In Proceedings of KDD’11. 1082--1090. Google ScholarDigital Library
- K. Dehnad. 1987. Density estimation for statistics and data analysis. Technometrics 29, 4 (1987), 495--495.Google ScholarCross Ref
- C. Desrosiers and G. Karypis. 2011. A comprehensive survey of neighborhood-based recommendation methods. In Recommender Systems Handbook. Springer, 107--144.Google Scholar
- R. P. Ebstein, O. Novick, R. Umansky, B. Priel, Y. Osher, D. Blaine, E. R. Bennett, L. Nemanov, M. Katz, and R. H. Belmaker. 1996. Dopamine D4 receptor (D4DR) exon III polymorphism associated with the human personality trait of novelty seeking. Nature Genetics 12, 1 (1996), 78--80.Google ScholarCross Ref
- V. Etter, M. Kafsi, and E. Kazemi. 2012. Been there, done that: What your mobility traces reveal about your behavior. Mobile Data Challenge by Nokia Workshop, in conjunction with Int. Conf. on Pervasive Computing.Google Scholar
- H. Gao, J. Tang, X. Hu, and H. Liu. 2013. Modeling temporal effects of human mobile behavior on location-based social networks. In Proceedings of CIKM’13. Google ScholarDigital Library
- H. Gao, J. Tang, and H. Liu. 2012a. Exploring social-historical ties on location-based social networks. In Proceedings of ICWSM’12.Google Scholar
- H. Gao, J. Tang, and H. Liu. 2012b. gSCorr: Modeling geo-social correlations for new check-ins on location-based social networks. In Proceedings of CIKM’12. ACM, 1582--1586. Google ScholarDigital Library
- H. Gao, J. Tang, and H. Liu. 2012c. Mobile location prediction in spatio-temporal context. In Proceedings of the Mobile Data Challenge at the 10th International Conference on Pervasive Computing.Google Scholar
- M. Ge, C. Delgado-Battenfeld, and D. Jannach. 2010. Beyond accuracy: Evaluating recommender systems by coverage and serendipity. In Proceedings of RecSys’10. ACM, 257--260. Google ScholarDigital Library
- M. C. Gonzalez, C. A. Hidalgo, and A. L. Barabasi. 2008. Understanding individual human mobility patterns. Nature 453, 7196 (2008), 779--782.Google Scholar
- T. Hastie, R. Tibshirani, and J. H. Friedman. 2001. The Elements of Statistical Learning. Vol. 1. Springer New York.Google Scholar
- N. Hurley and M. Zhang. 2011. Novelty and diversity in top-N recommendation--analysis and evaluation. ACM Transactions on Internet Technology (TOIT) 10, 4 (2011), 14. Google ScholarDigital Library
- D. Lian and X. Xie. 2011. Learning location naming from user check-in histories. In Proceedings of GIS’11. ACM, 112--121. Google ScholarDigital Library
- D. Lian, V. W. Zheng, and X. Xie. 2013. Collaborative filtering meets next check-in location prediction. In Proceedings of WWW’13 Companion. ACM, 231--232. Google ScholarDigital Library
- B. Liu and H. Xiong. 2013. Point-of-interest recommendation in location based social networks with topic and location awareness. In Proceedings of SDM’13. SIAM, 396--404.Google Scholar
- T. Y. Liu. 2009. Learning to rank for information retrieval. Foundations and Trends in Information Retrieval 3, 3 (2009), 225--331. Google ScholarDigital Library
- J. McInerney, S. Stein, A. Rogers, and N. R. Jennings. 2013a. Breaking the habit: Measuring and predicting departures from routine in individual human mobility. Pervasive and Mobile Computing 9, 6 (2013), 808--822. Google ScholarDigital Library
- J. McInerney, J. Zheng, A. Rogers, and N. R. Jennings. 2013b. Modelling heterogeneous location habits in human populations for location prediction under data sparsity. In Proceedings of Ubicomp’13. ACM, 469--478. Google ScholarDigital Library
- A. Monreale, F. Pinelli, R. Trasarti, and F. Giannotti. 2009. WhereNext: A location predictor on trajectory pattern mining. In Proceedings of KDD’09. ACM, 637--646. Google ScholarDigital Library
- A. Noulas, S. Scellato, N. Lathia, and C. Mascolo. 2012a. Mining user mobility features for next place prediction in location-based services. In Proceedings of ICDM’12. IEEE, 1038--1043. Google ScholarDigital Library
- A. Noulas, S. Scellato, N. Lathia, and C. Mascolo. 2012b. A random walk around the city: New venue recommendation in location-based social networks. In Proceedings of SocialCom’12. IEEE, 144--153. Google ScholarDigital Library
- A. Ofstad, E. Nicholas, R. Szcodronski, and R. R. Choudhury. 2008. Aampl: Accelerometer augmented mobile phone localization. In Proceedings of the fi1st ACM International Workshop on Mobile Entity Localization and Tracking in GPS-Less Environments. ACM, 13--18. Google ScholarDigital Library
- R. Pan, Y. Zhou, B. Cao, N. N. Liu, R. Lukose, M. Scholz, and Q. Yang. 2008. One-class collaborative filtering. In Proceedings of ICDM’08. IEEE, 502--511. Google ScholarDigital Library
- S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of UAI’09. AUAI Press, 452--461. Google ScholarDigital Library
- A. Sadilek, H. Kautz, and J. P. Bigham. 2012. Finding your friends and following them to where you are. In Proceedings of WSDM’12. ACM, 723--732. Google ScholarDigital Library
- G. Shani, D. Heckerman, and R. I. Brafman. 2005. An MDP-based recommender system. Journal of Machine Learning Research 6 (2005), 1265--1295. Google ScholarDigital Library
- B. Shaw, J. Shea, S. Sinha, and A. Hogue. 2013. Learning to rank for spatiotemporal search. In Proceedings of WSDM’13. ACM, 717--726. Google ScholarDigital Library
- C. Song, T. Koren, P. Wang, and A. L. Barabási. 2010a. Modelling the scaling properties of human mobility. Nature Physics 6, 10 (2010), 818--823.Google ScholarCross Ref
- C. Song, Z. Qu, N. Blumm, and A. L. Barabási. 2010b. Limits of predictability in human mobility. Science 327, 5968 (2010), 1018--1021.Google Scholar
- L. Song, D. Kotz, R. Jain, and X. He. 2004. Evaluating location predictors with extensive Wi-Fi mobility data. In Proceedings of INFOCOM’04, Vol. 2. IEEE, 1414--1424.Google Scholar
- M. Szell, R. Sinatra, G. Petri, S. Thurner, and V. Latora. 2012. Understanding mobility in a social petri dish. Scientific Reports 2 (2012).Google Scholar
- Y. W. Teh. 2006a. A Bayesian Interpretation of Interpolated Kneser-Ney. Technical Report TRA2/06. School of Computing, National University of Singapore.Google Scholar
- Y. W. Teh. 2006b. A hierarchical Bayesian language model based on Pitman-Yor processes. In Proceedings of ACL’06. ACL, 985--992. Google ScholarDigital Library
- W. R. Tobler. 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography 46 (1970), 234--240.Google ScholarCross Ref
- S. Vargas and P. Castells. 2011. Rank and relevance in novelty and diversity metrics for recommender systems. In Proceedings of RecSys’11. ACM, 109--116. Google ScholarDigital Library
- C. Wang and B. A. Huberman. 2012. How random are online social interactions? Scientific Reports 2 (2012).Google Scholar
- D. Yang, D. Zhang, Z. Yu, and Z. Wang. 2013. A sentiment-enhanced personalized location recommendation system. In Proceedings of the 24th ACM Conference on Hypertext and Social Media (HT’13). ACM, 119--128. Google ScholarDigital Library
- M. Ye, P. Yin, W.-C. Lee, and D.-L. Lee. 2011. Exploiting geographical influence for collaborative point-of-interest recommendation. In Proceedings of SIGIR’11. ACM, 325--334. Google ScholarDigital Library
- D. Zhang, C. Chen, Z. Zhou, and B. Li. 2012. Identifying logical location via GPS-enabled mobile phone and wearable camera. International Journal of Pattern Recognition and Artificial Intelligence (2012).Google Scholar
- Y. Zhang, M. Zhang, Y. Liu, S. Ma, and S. Feng. 2013. Localized matrix factorization for recommendation based on matrix block diagonal forms. In Proceedings of WWW’13. International World Wide Web Conferences Steering Committee, 1511--1520. Google ScholarDigital Library
- V. W. Zheng, B. Cao, Y. Zheng, X. Xie, and Q. Yang. 2010a. Collaborative filtering meets mobile recommendation: A user-centered approach. In Proceedings of AAAI’10. AAAI Press.Google Scholar
- V. W. Zheng, Y. Zheng, X. Xie, and Q. Yang. 2010b. Collaborative location and activity recommendations with gps history data. In Proceedings of WWW’10. ACM, 1029--1038. Google ScholarDigital Library
- Y. Zheng, L. Zhang, Z. Ma, X. Xie, and W. Y. Ma. 2011. Recommending friends and locations based on individual location history. ACM Transactions on the Web 5, 1 (2011), 5. Google ScholarDigital Library
Index Terms
- CEPR: A Collaborative Exploration and Periodically Returning Model for Location Prediction
Recommendations
Addressing the cold-start problem in location recommendation using geo-social correlations
Location-based social networks (LBSNs) have attracted an increasing number of users in recent years, resulting in large amounts of geographical and social data. Such LBSN data provide an unprecedented opportunity to study the human movement from their ...
gSCorr: modeling geo-social correlations for new check-ins on location-based social networks
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge managementLocation-based social networks (LBSNs) have attracted an increasing number of users in recent years. The availability of geographical and social information of online LBSNs provides an unprecedented opportunity to study the human movement from their ...
Utilization of Real Time Behavior and Geographical Attraction for Location Recommendation
Personalized location recommendation is an increasingly active topic in recent years, which recommends appropriate locations to users based on their temporal and geospatial visiting patterns. Current location recommendation methods usually estimate the ...
Comments