Where Is My Next Friend? Recommending Enjoyable Profiles in Location Based Services

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 644)

Abstract

How many of your friends, with whom you enjoy spending some time, live close by? How many people are at your reach, with whom you could have a nice conversation? We introduce a measure of enjoyability that may be the basis for a new class of location-based services aimed at maximizing the likelihood that two persons, or a group of people, would enjoy spending time together. Our enjoyability takes into account both topic similarity between two users and the users’ tendency to connect to people with similar or dissimilar interest. We computed the enjoyability on two datasets of geo-located tweets, and we reasoned on the applicability of the obtained results for producing friend recommendations. We aim at suggesting couples of users which are not friends yet, but which are frequently co-located and maximize our enjoyability measure. By taking into account the spatial dimension, we show how 50 % of users may find at least one enjoyable person within 10 km of their two most visited locations. Our results are encouraging, and open the way for a new class of recommender systems based on enjoyability.

Notes

Acknowledgments

This work has been partially supported by the European Commission under the SMARTCITIES Project n. FP7-ICT-609042, PETRA.

References

  1. 1.
    Aiello, L.M., Barrat, A., Cattuto, C., Ruffo, G., Schifanella, R.: Link creation and profile alignment in the anobii social network (2010) arXiv:1006.4966
  2. 2.
    Aiello, L.M., Barrat, A., Schifanella, R., Cattuto, C., Markines, B., Menczer, F.: Friendship prediction and homophily in social media. TWEB 6(2), 9 (2012)CrossRefGoogle Scholar
  3. 3.
    Al Zamal, F., Liu, W., Ruths, D.: Homophily and latent attribute inference: Inferring latent attributes of twitter users from neighbors. ICWSM, 270 (2012)Google Scholar
  4. 4.
    Berlingerio, M., Koutra, D., Eliassi-Rad, T., Faloutsos, C.: Network similarity via multiple social theories. In: Advances in Social Networks Analysis and Mining 2013, ASONAM’13, pp. 1439–1440. Niagara, ON, Canada, 25–29 Aug 2013Google Scholar
  5. 5.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATHGoogle Scholar
  6. 6.
    Carmagnola, F., Vernero, F., Grillo, P.: Sonars: a social networks-based algorithm for social recommender systems. In: UMAP, pp. 223–234. Springer (2009)Google Scholar
  7. 7.
    Chemudugunta, C., Steyvers, P.S.M.: Modeling general and specific aspects of documents with a probabilistic topic model. In: NIPS, vol. 19, p. 241 (2007)Google Scholar
  8. 8.
    Cici, B., Markopoulou, A., Frias-Martinez, E., Laoutaris, N.: Assessing the potential of ride-sharing using mobile and social data: a tale of four cities. In: Ubicomp, pp. 201–211 (2014)Google Scholar
  9. 9.
    De Choudhury, M.: Tie formation on twitter: homophily and structure of egocentric networks. In: PASSAT, pp. 465–470 (2011)Google Scholar
  10. 10.
    McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Ann. Rev. Sociol. 415–444 (2001)Google Scholar
  11. 11.
    Oldenburg, R.: The Great Good Place: Café, Coffee Shops, Community Centers, Beauty Parlors, General Stores, Bars, Hangouts, and How They Get You through the Day. Paragon House Publishers (1989)Google Scholar
  12. 12.
    Pedreschi, D.: Big data, social mining, diversity, and wellbeing. In: SIS, pp. 1–6 (2014)Google Scholar
  13. 13.
    Rakesh, V., Singh, D., Vinzamuri, B., Reddy, C.K.: Personalized recommendation of twitter lists using content and network information. In: ICWSM (2014)Google Scholar
  14. 14.
    Sun, J., Zhu, Y.: Microblogging personalized recommendation based on ego networks. WI-IAT 1, 165–170 (2013)Google Scholar
  15. 15.
    Teh, Y.W., Jordan, M.I., Beal, M.J., Blei, D.M.: Hierarchical dirichlet processes. J. Am. Stat. Assoc. 101(476) (2006)Google Scholar
  16. 16.
    Yuan, G., Murukannaiah, P.K., Zhang, Z., Singh, M.P.: Exploiting sentiment homophily for link prediction. In: RecSys, pp. 17–24 (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.KDDLabUniversity of PisaPisaItaly
  2. 2.IBM ResearchDublinIreland

Personalised recommendations