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A User-Side POIs Mobile Recommender System

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Advances in Computing Systems and Applications (CSA 2020)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 199))

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Abstract

Recommending pertinent Place Of Interests (POIs) is a desirable feature for mobile users, and which is generally served by for-profit proprietary platforms, such as Yelp, TripAdvisor, etc. However, the siloed design of these platforms raises today several issues about privacy, user data portability, and algorithm transparency. To address these issues, we propose a decoupled recommender system (RS) architecture. The idea consists of externalizing the sensitive features, such as the users’ preferences and the underlying RS algorithm, from the service’s application. Hence, the proposed RS operates as an interchangeable third-party service. We conducted several experiments to evaluate the impact of the decentralization, and we were able to improve the performances by relying on Linked Open Data (LOD) and on an appropriate similarity measure.

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Notes

  1. 1.

    https://www.yelp.com.

  2. 2.

    https://www.foursquare.com.

  3. 3.

    https://solid.mit.edu.

  4. 4.

    https://solid.inrupt.com.

  5. 5.

    http://dbpedia.org.

  6. 6.

    http://linkedgeodata.org/About.

  7. 7.

    http://linkedgeodata.org/sparql.

  8. 8.

    https://www.dbpedia-spotlight.org/demo/.

  9. 9.

    https://www.yelp.com/developers/documentation/v3/business_search.

  10. 10.

    https://www.yelp.com/dataset/challenge.

  11. 11.

    https://en.wikipedia.org/wiki/Student’s_t-test.

  12. 12.

    https://en.wikipedia.org/wiki/Statistical_significance.

References

  1. Berners-Lee, T.: The web is under threat. Join us and fight for it. (2018). https://webfoundation.org/2018/03/web-birthday-29. Accessed 21 Dec 2019

  2. Boja, U., Passant, A.: Weaving SIOC into the web of linked data. In: Proceedings of the Workshop on Linked Data on the Web (2008)

    Google Scholar 

  3. Boubenia, M., Belkhir, A., Bouyakoub, M.F.: A multi-level approach for mobile recommendation of services. In: Proceedings of the International Conference on Internet of Things and Cloud Computing, p. 40. ACM (2016). https://doi.org/10.1145/2896387.2896425

  4. Cadwalladr, C., Graham-Harrison, E.: Revealed: 50 million Facebook profiles harvested for Cambridge Analytica in major data breach (Mar 2018). https://www.theguardian.com/news/2018/mar/17/cambridge-analytica-facebook-influence-us-election. Accessed 21 Dec 2019

  5. Cheniki, N., Belkhir, A., Sam, Y., Messai, N.: LODS: a linked open data based similarity measure. In: 2016 IEEE 25th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises, pp. 229–234. IEEE (2016). https://doi.org/10.1109/WETICE.2016.58

  6. Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 39–46. ACM (2010). https://doi.org/10.1145/1864708.1864721

  7. Heckmann, D., Schwartz, T., Brandherm, B., Schmitz, M., von Wilamowitz-Moellendorff, M.: Gumo–the general user model ontology. In: User Modeling 2005, pp. 428–432. Springer, Heidelberg (2005). https://doi.org/10.1007/11527886_58

  8. Hochmair, H.H., Juhász, L., Cvetojevic, S.: Data quality of points of interest in selected mapping and social media platforms. In: LBS 2018: 14th International Conference on Location Based Services, pp. 293–313. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-71470-7_15

  9. Jaccard, P.: Étude comparative de la distribution florale dans une portion des Alpes et du Jura. Bulletin de la Société Vaudoise des Sciences Naturelles 37, 547–579 (1901). https://doi.org/10.5169/seals-266450

    Article  Google Scholar 

  10. Lops, P., de Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 73–105. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-85820-3_3

  11. O’Donnell, A.: Why Sharing Your Location on Social Media Is a Bad Thing (2018). https://www.lifewire.com/why-sharing-your-location-on-social-media-is-a-bad-thing-2487165. Accessed 21 Dec 2019

  12. Piao, G., Breslin, J.G.: Analyzing aggregated semantics-enabled user modeling on Google+ and Twitter for personalized link recommendations. In: Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, pp. 105–109. ACM (2016). https://doi.org/10.1145/2930238.2930278

  13. Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-85820-3

    Book  MATH  Google Scholar 

  14. Riedl, J.: Personalization and privacy. IEEE Internet Comput. 5(6), 29–31 (2001). https://doi.org/10.1109/4236.968828

    Article  Google Scholar 

  15. Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975). https://doi.org/10.1145/361219.361220

    Article  MATH  Google Scholar 

  16. Verborgh, R.: Re-decentralizing the web, for good this time. In: Seneviratne, O., Hendler, J. (eds.) Linking the World’s Information: Tim Berners-Lee’s Invention of the World Wide Web. ACM (2019). https://ruben.verborgh.org/articles/redecentralizing-the-web/

  17. Zhang, J.: Anchoring effects of recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 375–378. ACM (2011). https://doi.org/10.1145/2043932.2044010

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Correspondence to Mohamed Boubenia .

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Boubenia, M., Bouyakoub, F.M., Belkhir, A. (2021). A User-Side POIs Mobile Recommender System. In: Senouci, M.R., Boudaren, M.E.Y., Sebbak, F., Mataoui, M. (eds) Advances in Computing Systems and Applications. CSA 2020. Lecture Notes in Networks and Systems, vol 199. Springer, Cham. https://doi.org/10.1007/978-3-030-69418-0_18

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