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Applied Intelligence

, Volume 45, Issue 4, pp 1047–1065 | Cite as

Making recommendations by integrating information from multiple social networks

  • Makbule Gulcin Ozsoy
  • Faruk Polat
  • Reda AlhajjEmail author
Article

Abstract

It is becoming a common practice to use recommendation systems to serve users of web-based platforms such as social networking platforms, review web-sites, and e-commerce web-sites. Each platform produces recommendations by capturing, maintaining and analyzing data related to its users and their behavior. However, people generally use different web-based platforms for different purposes. Thus, each platform captures its own data which may reflect certain aspects related to its users. Integrating data from multiple platforms may widen the perspective of the analysis and may help in modeling users more effectively. Motivated by this, we developed a recommendation framework which integrates data collected from multiple platforms. For this purpose, we collected and anonymized datasets which contain information from several social networking and social media platforms, namely BlogCatalog, Twitter, Flickr, Facebook, YouTube and LastFm. The collected and integrated data forms a consolidated repository that may become a valuable source for researchers and practitioners. We implemented a number of recommendation methodologies to observe their performance for various cases which involve using single versus multiple features from a single source versus multiple sources. The conducted experiments have shown that using multiple features from multiple sources is expected to produce a more concrete and wider perspective of user’s behavior and preferences. This leads to improved recommendation outcome.

Keywords

Recommendation systems Individual modeling Multiple data sources Social networking platforms Multiple perspective based analysis User behavior 

Notes

Acknowledgments

This research is supported by TUBITAK-BIDEB 2214/A program.

References

  1. 1.
    Bobadilla J, Ortega F, Hernando A, Gutirrez A (2013) Recommender systems survey. Knowl-Based Syst 46:109–132. doi: 10.1016/j.knosys.2013.03.012. ISSN 0950-7051. http://www.sciencedirect.com/science/article/pii/S0950705113001044
  2. 2.
    Liu H, Maes P (2005) Interestmap: Harvesting social network profiles for recommendations. Beyond Personalization 2005:54Google Scholar
  3. 3.
    Wu I-C, Niu Y-F (2015) Effects of anchoring process under preference stabilities for interactive movie recommendations. Journal of the Association for Information Science and Technology 66(8):1673–1695. doi: 10.1002/asi.23280. ISSN 2330-1643CrossRefGoogle Scholar
  4. 4.
    Motoyama M, Varghese G (2009) I seek you: Searching and matching individuals in social networks. In: Proceedings of the Eleventh International Workshop on Web Information and Data Management, WIDM ’09. ISBN 978-1-60558-808-7. ACM, New York, pp 67–75Google Scholar
  5. 5.
    Zafarani R, Liu H (2013) Connecting users across social media sites: A behavioral-modeling approach. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’13. ISBN 978-1-4503-2174-7. ACM, New York, pp 41–49Google Scholar
  6. 6.
    Jain P, Kumaraguru P, Joshi A @i seek ‘fb.me’: Identifying users across multiple online social networks. In: Proceedings of the 22Nd International Conference on World Wide Web Companion, WWW ’13 Companion, pages 1259–1268, Republic and Canton of Geneva, Switzerland, 2013. International World Wide Web Conferences Steering Committee. ISBN 978-1-4503- 2038-2Google Scholar
  7. 7.
    Tan S, Guan Z, Cai D, Qin X, Jiajun B u, Chen C (2014a) Mapping users across networks by manifold alignment on hypergraph. In: Proceedings of the twenty-eighth AAAI conference on artificial intelligence, July 27-31, 2014, Québec City, Québec, Canada, pp 159–165Google Scholar
  8. 8.
    Tan S, Bu J, Qin X, Chen C, Cai D (2014b) Cross domain recommendation based on multi-type media fusion. Neurocomput 127:124–134. ISSN 0925-2312CrossRefGoogle Scholar
  9. 9.
    Zhang Y (2014) Browser-oriented universal cross-site recommendation and explanation based on user browsing logs. In: Proceedings of the 8th ACM Conference on Recommender Systems, RecSys ’14. ISBN 978-1-4503-2668-1. ACM, New York, pp 433–436Google Scholar
  10. 10.
    Kumar A, Kumar N, Hussain M, Chaudhury S, Agarwal S (2014) Semantic clustering-based cross-domain recommendation, pp 137–141Google Scholar
  11. 11.
    Tan EM-Y, Goh DH-L (2015) A study of social interaction during mobile information seeking. Journal of the Association for Information Science and Technology 66(10):2031–2044. doi: 10.1002/asi.23310. ISSN 2330-1643CrossRefGoogle Scholar
  12. 12.
    Gao H, Tang J, Xia H u, Liu H (2013) Exploring temporal effects for location recommendation on location-based social networks. In: Proceedings of the 7th ACM conference on recommender systems. ACM, pp 93–100Google Scholar
  13. 13.
    Ye M, Yin P, Lee W-C, Lee DL (2011) Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceeding of the 34th international ACM SIGIR conference on research and development in information retrieval, SIGIR 2011, Beijing, China, July 25–29, 2011. doi: 10.1145/2009916.2009962, pp 325–334
  14. 14.
    Ye M, Yin P, Lee W-C (2010) Location recommendation for location-based social networks. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS ’10. ACM, New York, pp 458–461Google Scholar
  15. 15.
    Ma H, Zhou D, Liu C, Lyu MR, King I (2011) Recommender systems with social regularization. In: Proceedings of the Forth International Conference on Web Search and Web Data Mining, WSDM 2011, Hong Kong, China, February, 9–12, 2011. doi: 10.1145/1935826.1935877, pp 287–296
  16. 16.
    Ozsoy MGO, Polat F, Alhajj R (2014) Multi-objective optimization based location and social network aware recommendation. In: 10Th IEEE international conference on collaborative computing: Networking, Applications and Worksharing, CollaborateCom 2014, Miami, Florida, USA, October 22–25, 2014. doi: 10.4108/icst.collaboratecom.2014.257382, pp 233–242
  17. 17.
    ESWC-14 Challenge (2014) Eswc-14 challenge: Linked open data-enabled recommender systems. http://challenges.2014.eswc-conferences.org/index.php/RecSys
  18. 18.
    Ozsoy MGO, Polat F, Alhajj R (2015) Modeling individuals and making recommendations using multiple social networks. In: International symposium on foundations and applications of big data analytics, FAB ’15, pp 1–1Google Scholar
  19. 19.
    Maleszka M, Mianowska B, Nguyen NT (2013) A method for collaborative recommendation using knowledge integration tools and hierarchical structure of user profiles. Knowl-Based Syst 47:1–13. doi: 10.1016/j.knosys.2013.02.016. ISSN 0950-7051. http://www.sciencedirect.com/science/article/pii/S0950705113000890
  20. 20.
    Gao H, Tang J, Liu H (2012) Exploring social-historical ties on location-based social networks. In: Proceedings of the Sixth International Conference on Weblogs and Social Media, Dublin, Ireland, June 4–7, 2012Google Scholar
  21. 21.
    Burke RD (2002) Hybrid recommender systems: Survey and experiments. User Model User-adapt Interact 12 (4):331–370. doi: 10.1023/A:1021240730564 CrossRefzbMATHGoogle Scholar
  22. 22.
    Zhang J, Chow C (2015) Ticrec: A probabilistic framework to utilize temporal influence correlations for time-aware location recommendations (to be published). IEEE Trans Serv Comput PP(99):1–1Google Scholar
  23. 23.
    Huang C-L, Yeh P-H, Lin C-W, Wu D-C (2014) Utilizing user tag-based interests in recommender systems for social resource sharing websites. Knowl-Based Syst 56:86–96. doi: 10.1016/j.knosys.2013.11.001. ISSN 0950-7051. http://www.sciencedirect.com/science/article/pii/S0950705113003511
  24. 24.
    Shapira B, Zabar B (2011) Personalized search: Integrating collaboration and social networks. Journal of the American Society for Information Science and Technology 62(1):146–160. doi: 10.1002/asi.21446. ISSN 1532-2890CrossRefGoogle Scholar
  25. 25.
    Yuan Q, Cong G, Ma Z, Sun A, Thalmann NM (2013) Time-aware point-of-interest recommendation. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’13. ISBN 978-1-4503-2034-4. ACM, New York, pp 363–372Google Scholar
  26. 26.
    Quijano-Snchez L, Daz-Agudo B, Recio-Garca JA (2014) Development of a group recommender application in a social network. Knowledge-Based Systems 71:72–85. doi: 10.1016/j.knosys.2014.05.013  10.1016/j.knosys.2014.05.013. ISSN 0950-7051. http://www.sciencedirect.com/science/article/pii/S095070511400197X
  27. 27.
    Liu X, Liu Y, Aberer K, Miao C (2013) Personalized point-of-interest recommendation by mining users’ preference transition. In: Proceedings of the 22Nd ACM International Conference on Conference on Information Knowledge Management, CIKM ’13. ISBN 978-1-4503-2263-8. ACM, New York, pp 733–738Google Scholar
  28. 28.
    Hu B, Jamali M, Ester M (2013a) Spatio-temporal topic modeling in mobile social media for location recommendation. In: 2013 IEEE 13th international conference on Data mining (ICDM). IEEE, pp 1073–1078Google Scholar
  29. 29.
    Yuan Q, Cong G, Sun A (2014) Graph-based point-of-interest recommendation with geographical and temporal influences. In: Proceedings of the 23rd ACM international conference on conference on information and knowledge management. ACM, pp 659– 668Google Scholar
  30. 30.
    Tsai C-Y, Lai B-H (2015) A location-item-time sequential pattern mining algorithm for route recommendation. Knowl-Based Syst 73:97–110CrossRefGoogle Scholar
  31. 31.
    Levandoski JJ, Sarwat M, Eldawy A, Mokbel MF (2012) LARS: A Location-aware recommender systemGoogle Scholar
  32. 32.
    Zheng VW, Zheng Y u, Xie X, Yang Q (2010) Collaborative location and activity recommendations with GPS history data. In: Proceedings of the 19th international conference on world wide web, WWW 2010, raleigh, north carolina, USA, April 26-30, 2010. doi: 10.1145/1772690.1772795, pp 1029–1038
  33. 33.
    Cheng C, Yang H, Lyu MR, King I (2013) Where you like to go next: Successive point-of-interest recommendation. In: IJCAI 2013, Proceedings of the 23rd international joint conference on artificial intelligence, beijing, China, August 3–9, 2013Google Scholar
  34. 34.
    Liu X, Aberer K (2013) Soco: a social network aided context-aware recommender system. In: 22Nd international world wide web conference, WWW ’13, rio de janeiro, Brazil, May 13–17, 2013, pp 781–802Google Scholar
  35. 35.
    Lakiotaki K, Tsafarakis S, Matsatsinis NF (2008) Uta-rec: a recommender system based on multiple criteria analysis. In: Proceedings of the 2008 ACM conference on recommender systems, recsys 2008, lausanne, Switzerland, October 23–25, 2008. doi: 10.1145/1454008.1454043, pp 219–226
  36. 36.
    Manouselis N, Costopoulou C (2007) Experimental analysis of design choices in multiattribute utility collaborative filtering. IJPRAI 21(2):311–331. doi: 10.1142/S021800140700548X Google Scholar
  37. 37.
    Lee H-H, Teng W-G Incorporating multi-criteria ratings in recommendation systems. In: Proceedings of the IEEE International Conference on Information Reuse and Integration, IRI 2007, 13–15, August 2007, Las vegas. doi: 10.1109/IRI.2007.4296633, vol 2007, pp 273–278
  38. 38.
    Ortega F, Sánchez JL, Bobadilla J, Gutiérrez A (2013) Improving collaborative filtering-based recommender systems results using pareto dominance. Inf. Sci. 239:50–61. doi: 10.1016/j.ins.2013.03.011 CrossRefGoogle Scholar
  39. 39.
    Di Noia T, Cantador I, Ostuni VC (2014) Linked open data-enabled recommender systems: Eswc 2014 challenge on book recommendation. In: Presutti V, Stankovic M, Cambria E, Cantador I, Di Iorio A, Di Noia T, Lange C, Recupero DR, Tordai A (eds) Semantic Web Evaluation Challenge, volume 475 of Communications in Computer and Information Science. ISBN 978-3-319-12023-2. Springer International Publishing, pp 129–143Google Scholar
  40. 40.
    Winoto P, Tang T (2008) If you like the devil wears prada the book, will you also enjoy the devil wears prada the movie? a study of cross-domain recommendations. New Generation Computing 26(3):209–225. ISSN 0288-3635CrossRefGoogle Scholar
  41. 41.
    Hu L, Cao J, Xu G, Cao L, Gu Z, Zhu C Personalized recommendation via cross-domain triadic factorization. In: Proceedings of the 22Nd International Conference on World Wide Web, WWW ’13, pages 595–606, Republic and Canton of Geneva, Switzerland, 2013b. International World Wide Web Conferences Steering Committee. ISBN 978-1-4503-2035-1Google Scholar
  42. 42.
    Loni B, Shi Y, Larson M, Hanjalic A (2014) Cross-domain collaborative filtering with factorization machines. In: Proceedings of the Advances in information retrieval - 36th european conference on IRResearch, ECIR 2014, amsterdam, the Netherlands, April 13–16, 2014, pp 656–661Google Scholar
  43. 43.
    Li C-Y, Lin S-D (2014) Matching users and items across domains to improve the recommendation quality. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14. ISBN 978-1-4503-2956-9. ACM, New York, pp 801–810Google Scholar
  44. 44.
    Frick M (2015) Big data and its epistemology. Journal of the Association for Information Science and Technology 66(4):651–661. doi: 10.1002/asi.23212. ISSN 2330-1643MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Makbule Gulcin Ozsoy
    • 1
  • Faruk Polat
    • 1
  • Reda Alhajj
    • 2
    • 3
    Email author
  1. 1.Department of Computer EngineeringMiddle East Technical UniversityAnkaraTurkey
  2. 2.Department of Computer ScienceUniversity of CalgaryCalgaryCanada
  3. 3.Department of Computer ScienceGlobal UniversityBeirutLebanon

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