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Location Recommendation with Social Media Data

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10100))

Abstract

Smartphones with inbuilt location-sensing technologies are now creating a new realm for recommender systems research and pratice. In this chapter, we focus on recommender systems that use location data to help users navigate the physical world. We examine various recommendation problems: recommending new places, recommending the next place to visit, events to attend, and recommending neighbourhoods or large areas to explore further. Lastly, we discuss how (personalized) place search is analogous to web search. For each of these domains, we present relevant data, algorithms, and methods, and we illustrate how researchers are investigating them with examples from the literature. We close by summarizing key aspects and suggesting future directions.

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Notes

  1. 1.

    http://www.dia.uniroma3.it/~ailab/?page_id=97.

  2. 2.

    www.yelp.com.

  3. 3.

    www.tripadvisor.com.

  4. 4.

    www.delicious.com.

References

  1. Abowd, G.D., Dey, A.K., Brown, P.J., Davies, N., Smith, M., Steggles, P.: Towards a better understanding of context and context-awareness. In: Gellersen, H.-W. (ed.) HUC 1999. LNCS, vol. 1707, pp. 304–307. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48157-5_29

    Chapter  Google Scholar 

  2. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)

    Article  Google Scholar 

  3. Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 217–253. Springer, Boston (2011). https://doi.org/10.1007/978-1-4899-7637-6_6

    Chapter  MATH  Google Scholar 

  4. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD Record, vol. 22, pp. 207–216. ACM (1993)

    Article  Google Scholar 

  5. Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the Eleventh International Conference on Data Engineering, pp. 3–14. IEEE (1995)

    Google Scholar 

  6. Amatriain, X., Pujol, J.: Data mining methods for recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-85820-3_2

    Chapter  Google Scholar 

  7. Ankerst, M., Breunig, M., Kriegel, H., Sander, J.: OPTICS: ordering points to identify the clustering structure. In: ACM SIGMOD, Philadelphia, USA (1999)

    Google Scholar 

  8. Bao, J., Zheng, Y., Wilkie, D., Mokbel, M.: Recommendations in location-based social networks: a survey. GeoInformatica 19(3), 525–565 (2015)

    Article  Google Scholar 

  9. Baraglia, R., Muntean, C.I., Nardini, F.M., Silvestri, F.: LearNext: learning to predict tourists movements. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, pp. 751–756. ACM (2013)

    Google Scholar 

  10. Becker, R., Caceres, R., Hanson, K., Isaacman, S., Loh, J., Martonosi, M., Rowland, J., Urbanek, S., Varshavsky, A., Volisky, C.: Human mobility characterization from cellular network data. Commun. ACM 56(1), 74–82 (2013)

    Article  Google Scholar 

  11. Biancalana, C., Gasparetti, F., Micarelli, A., Sansonetti, G.: An approach to social recommendation for context-aware mobile services. ACM Trans. Intell. Syst. Technol. 4(1), 10:1–10:31 (2013)

    Article  Google Scholar 

  12. Bothorel, C., Picot-Clemente, R., Simon, G., Li, Z., Michiardi, P., Hadjadj-Aoul, Y., Garnier, J.: Technical report: preliminary report on CDN/dCDN modeling and analysis. ANR Project Vipeer, Deliverable 44 (2012)

    Google Scholar 

  13. Brockmann, D., Hufnagel, L., Geisel, T.: The scaling laws of human travel. Nature 439(7075), 462–465 (2006)

    Article  Google Scholar 

  14. Brusilovsky, P., Smyth, B., Shapira, B.: Social search. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 213–276. Springer, Cham (2018)

    Google Scholar 

  15. Chand, C., Thakkar, A., Ganatra, A.: Sequential pattern mining: survey and current research challenges. Int. J. Soft Comput. Eng. 2(1), 185–193 (2012)

    Google Scholar 

  16. Cohen, W.W., Schapire, R.E., Singer, Y.: Learning to order things. J. Artif. Intell. Res. 10(1), 243–270 (1999)

    MathSciNet  MATH  Google Scholar 

  17. Crandall, D., Backstrom, L., Huttenlocher, D., Kleinberg, J.: Mapping the world’s photos. In: WWW, Madrid, Spain, April 2009

    Google Scholar 

  18. Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Pers. Ubiquit. Comput. 10, 255–268 (2006)

    Article  Google Scholar 

  19. Farzan, R., Brusilovsky, P.: Social navigation. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 142–180. Springer, Cham (2018)

    Google Scholar 

  20. Fernandez-Tobias, I., Cantador, I., Kaminskas, M., Ricci, F.: A generic semantic-based framework for cross-domain recommendation. In: Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems, Chicago, USA (2011)

    Google Scholar 

  21. Forsati, R., Meybodi, M., Neiat, A.G.: Web page personalization based on weighted association rules. In: 2009 International Conference on Electronic Computer Technology, pp. 130–135. IEEE (2009)

    Google Scholar 

  22. Froehlich, J., Chen, M.Y., Smith, I.E., Potter, F.: Voting with your feet: an investigative study of the relationship between place visit behavior and preference. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 333–350. Springer, Heidelberg (2006). https://doi.org/10.1007/11853565_20

    Chapter  Google Scholar 

  23. Gao, H., Tang, J., Liu, H.: gSCorr: modeling geo-social correlations for new check-ins on location-based social networks. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM 2012, pp. 1582–1586. ACM, New York (2012)

    Google Scholar 

  24. Georgiev, P., Noulas, A., Mascolo, C.: The call of the crowd: event participation in location-based social services. In: Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media, Ann Arbour, USA, June 2014

    Google Scholar 

  25. Georgiev, P., Noulas, A., Mascolo, C.: Where businesses thrive: predicting the impact of the olympic games on local retailers through location-based services data. In: Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media, Ann Arbour, USA, June 2014

    Google Scholar 

  26. Guy, I.: People recommendation on social media. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 570–623. Springer, Cham (2018)

    Google Scholar 

  27. Hussain, F., Liu, H., Lu, H.: Relative measure for mining interesting rules. In: Proceedings of the Fourth European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2000, pp. 117–132. Citeseer (2000)

    Google Scholar 

  28. Jannach, D., Lerche, L., Zanker, M.: Recommending based on implicit feedback. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 510–569. Springer, Cham (2018)

    Google Scholar 

  29. Karamshuk, D., Noulas, A., Scellato, S., Nicosia, V., Mascolo, C.: Geo-spotting: mining online location-based services for optimal retail store placement. In: Proceedings of 19th ACM International Conference on Knowledge Discovery and Data Mining, Chicago, USA (2013)

    Google Scholar 

  30. Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)

    Article  MATH  Google Scholar 

  31. Kennedy, L., Naaman, M., Ahern, S., Nair, R., Rattenbury, T.: How flickr helps us make sense of the world: context and content in community-contributed media collections. In: ACM MM, Augsburg, Germany, September 2007

    Google Scholar 

  32. Kluver, D., Ekstrand, M., Konstan, J.: Rating-based collaborative filtering: algorithms and evaluation. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, pp. 344–390. Springer, Cham (2018)

    Google Scholar 

  33. Lathia, N., Capra, L.: Mining mobility data to minimise travellers’ spending on public transport. In: ACM KDD, San Diego, California, August 2011

    Google Scholar 

  34. Lathia, N., Froehlich, J., Capra, L.: Mining public transport usage for personalised intelligent transport systems. In: IEEE ICDM, Sydney, Australia, December 2010

    Google Scholar 

  35. Lee, D., Brusilovsky, P.: Recommendations based on social links. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, pp. 391–440. Springer, Cham (2018).

    Google Scholar 

  36. Levandoski, J.J., Sarwat, M., Eldawy, A., Mokbel, M.F.: LARS: a location-aware recommender system. In: 2012 IEEE 28th International Conference on Data Engineering (ICDE), pp. 450–461. IEEE (2012)

    Google Scholar 

  37. Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., Ma, W.: Mining user similarity based on location history. In: International Conference on Advances in Geographic Information Systems, Santa Ana, USA (2008)

    Google Scholar 

  38. Lian, D., Zheng, V.W., Xie, X.: Collaborative filtering meets next check-in location prediction. In: Proceedings of the 22nd International Conference on World Wide Web Companion, pp. 231–232. International World Wide Web Conferences Steering Committee (2013)

    Google Scholar 

  39. Lindqvist, J., Cranshaw, J., Wiese, J., Jong, J., Zimmerman, J.: I’m the mayor of my house: examining why people use foursquare - a social-driven location sharing application. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2409–2418. ACM (2011)

    Google Scholar 

  40. Liu, X., Liu, Y., Aberer, K., Miao, C.: Personalized point-of-interest recommendation by mining users’ preference transition. In: Proceedings of the 22nd ACM International Conference on Conference on Information and Knowledge Management, pp. 733–738. ACM (2013)

    Google Scholar 

  41. Mobasher, B., Dai, H., Luo, T., Nakagawa, M.: Effective personalization based on association rule discovery from web usage data. In: Proceedings of the 3rd International Workshop on Web Information and Data Management, pp. 9–15. ACM (2001)

    Google Scholar 

  42. Noulas, A., Scellato, S., Lathia, N., Mascolo, C.: Mining user mobility features for next place prediction in location-based services. In: IEEE International Conference on Data Mining, ICDM 2012 (2012)

    Google Scholar 

  43. Noulas, A., Scellato, S., Lathia, N., Mascolo, C.: A random walk around the city: new venue recommendation in location-based social networks. In: Proceedings of the 2012 ASE/IEEE International Conference on Social Computing and 2012 ASE/IEEE International Conference on Privacy, Security, Risk and Trust, SOCIALCOM-PASSAT 2012, pp. 144–153. IEEE Computer Society, Washington, D.C. (2012)

    Google Scholar 

  44. Noulas, A., Scellato, S., Mascolo, C., Pontil, M.: An empirical study of geographic user activity patterns in foursquare. In: Adamic, L.A., Baeza-Yates, R.A., Counts, S. (eds.) ICWSM. The AAAI Press (2011)

    Google Scholar 

  45. O’Mahoney, M., Smyth, B.: From opinions to recommendations. In: Brusilovsky, P., He, D. (eds.) Social Information Access, LNCS. LNCS, vol. 10100, pp. 480–509. Springer, Cham (2018)

    Google Scholar 

  46. Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D.: Context aware computing for the internet of things: a survey. IEEE Commun. Surv. Tutor. 16(1), 414–454 (2014)

    Article  Google Scholar 

  47. Picot-Clemente, R., Bothorel, C.: Recommendation of shopping places based on social and geographical influences. In: 5th ACM RecSys Workshop on Recommender Systems and the Social Web, RSWeb 2013, Hong Kong, Hong Kong SAR China, October 2013

    Google Scholar 

  48. Picot-Clemente, R., Bothorel, C., Lenca, P.: Contextual recommender system on a location-based social network for shopping places recommendation using association rules mining. In: The 6th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2014, vol. 551, pp. 3–13. Springer, Cham (2014)

    Google Scholar 

  49. Quercia, D., Lathia, N., Calabrese, F., Lorenzo, G.D., Crowcroft, J.: Recommending social events from mobile phone location data. In: IEEE ICDM, Sydney, Australia, December 2010

    Google Scholar 

  50. Quercia, D., Schifanella, R., Aiello, L.M.: The shortest path to happiness: recommending beautiful, quiet, and happy routes in the city. In: Proceedings of the 25th ACM Conference on Hypertext and Social Media, HT 2014, pp. 116–125. ACM, New York (2014). http://doi.acm.org/10.1145/2631775.2631799

  51. Quinlan, J.: Learning with continuous classes. In: AI 1992 (1992)

    Google Scholar 

  52. Rachuri, K., Mascolo, C., Musolesi, M.: Energy-accuracy trade-offs of sensor sampling in smart phone based sensing systems. In: Lovett, T., O’Neill, E. (eds.) Mobile Context Awareness: Capabilities Challenges and Applications Workshop. Springer, Copenhagen (2010). https://doi.org/10.1007/978-0-85729-625-2_3

    Chapter  Google Scholar 

  53. Rattenbury, T., Good, N., Naaman, M.: Toward automatic extraction of event and place semantics from flickr tags. In: ACM SIGIR, pp. 103–110, July 2007

    Google Scholar 

  54. Ratti, C., Pulselli, R., Williams, S., Frenchman, D.: Mobile landscapes: using location data from cell phones for urban analysis. Environ. Plann. B 33(5), 727–748 (2006)

    Article  Google Scholar 

  55. Shaw, B., Shea, J., Sinha, S., Hogue, A.: Learning to rank for spatiotemporal search. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 717–726. ACM (2013)

    Google Scholar 

  56. Sohn, T., et al.: Mobility detection using everyday GSM traces. In: Dourish, P., Friday, A. (eds.) UbiComp 2006. LNCS, vol. 4206, pp. 212–224. Springer, Heidelberg (2006). https://doi.org/10.1007/11853565_13

    Chapter  Google Scholar 

  57. Tai, C.H., Yang, D.N., Lin, L.T., Chen, M.S.: Recommending personalized scenic itinerarywith geo-tagged photos. In: 2008 IEEE International Conference on Multimedia and Expo, pp. 1209–1212. IEEE (2008)

    Google Scholar 

  58. Wei, L.Y., Zheng, Y., Peng, W.C.: Constructing popular routes from uncertain trajectories. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 195–203. ACM (2012)

    Google Scholar 

  59. Yahi, A., Chassang, A., Raynaud, L., Duthil, H., Chau, D.H.P.: Aurigo: an interactive tour planner for personalized itineraries. In: Proceedings of the 20th International Conference on Intelligent User Interfaces, IUI 2015, pp. 275–285. ACM, New York (2015). http://doi.acm.org/10.1145/2678025.2701366

  60. Yang, S.J., Zhang, J., Chen, I.Y.: A JESS-enabled context elicitation system for providing context-aware web services. Expert Syst. Appl. 34(4), 2254–2266 (2008)

    Article  Google Scholar 

  61. Yang, X.Y., Liu, Z., Fu, Y.: Mapreduce as a programming model for association rules algorithm on Hadoop. In: 2010 3rd International Conference on Information Sciences and Interaction Sciences (ICIS), pp. 99–102. IEEE (2010)

    Google Scholar 

  62. Ye, M., Yin, P., Lee, W.C., Lee, D.L.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, pp. 325–334. ACM, New York (2011)

    Google Scholar 

  63. Yoon, H., Zheng, Y., Xie, X., Woo, W.: Smart itinerary recommendation based on user-generated GPS trajectories. In: Yu, Z., Liscano, R., Chen, G., Zhang, D., Zhou, X. (eds.) UIC 2010. LNCS, vol. 6406, pp. 19–34. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16355-5_5

    Chapter  Google Scholar 

  64. Yoon, H., Zheng, Y., Xie, X., Woo, W.: Social itinerary recommendation from user-generated digital trails. Pers. Ubiquit. Comput. 16(5), 469–484 (2012)

    Article  Google Scholar 

  65. Zhang, A., Noulas, A., Scellato, S., Mascolo, C.: Hoodsquare: modeling and recommending neighbourhoods in location-based social networks. In: IEEE SocialCom, Washington D.C., September 2013

    Google Scholar 

  66. Zhang, J.D., Chow, C.Y.: iGSLR: personalized geo-social location recommendation: a kernel density estimation approach. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2013, pp. 334–343. ACM, New York (2013)

    Google Scholar 

  67. Zheng, V., Zheng, Y., Xie, X., Yang, Q.: Collaborative location and activity recommendations with GPS history data. In: ACM Proceedings of the 19th International Conference on World Wide Web, Raleigh, North Carolina, pp. 1029–1038, April 2010

    Google Scholar 

  68. Zheng, Y., Li, Q., Chen, Y., Xie, X., Ma, W.: Understanding mobility based on GPS data. In: ACM Ubicomp, Seoul, Korea (2008)

    Google Scholar 

  69. Zheng, Y., Zhang, L., Xie, X., Ma, W.: Mining interesting locations and travel sequences from GPS trajectories. In: WWW, Madrid, Spain, April 2008

    Google Scholar 

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Author information

Authors and Affiliations

  1. IMT Atlantique, Lab-STICC, Univ. Bretagne Loire, 29238, Brest, France

    Cécile Bothorel

  2. Skyscanner, Edinburgh, UK

    Neal Lathia

  3. Saagie, Le Petit-Quevilly, France

    Romain Picot-Clemente

  4. School of Computing and Communications, University of Lancaster, Lancaster, UK

    Anastasios Noulas

Authors
  1. Cécile Bothorel
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  2. Neal Lathia
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  3. Romain Picot-Clemente
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  4. Anastasios Noulas
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Corresponding author

Correspondence to Romain Picot-Clemente .

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Editors and Affiliations

  1. University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Peter Brusilovsky

  2. University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Daqing He

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Bothorel, C., Lathia, N., Picot-Clemente, R., Noulas, A. (2018). Location Recommendation with Social Media Data. In: Brusilovsky, P., He, D. (eds) Social Information Access. Lecture Notes in Computer Science(), vol 10100. Springer, Cham. https://doi.org/10.1007/978-3-319-90092-6_16

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