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Investigating Recommender Systems in OSNs

Model of Recommender Systems
  • Jana ShafiEmail author
  • Amtul Waheed
  • P. Venkata Krishna
Chapter
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Abstract

With the initiation of online social networks, the recommendation has arisen with the based approach to social network. This method approves a socialize networks amongst operators also creates references for a user founded on user’s assessments which effect indirect-direct socialize relationships with the specified user. A recommender system is a software system meant to make recommendations. Today Recommender based systems are attractive chosen tools to pick the online data appropriate to the users. To accomplish it, recommender system sorts numerous components, such as: processing and data collection, recommender model, a user interface and recommendation post-processing. A pioneering clue, enables aids to participate these zones, also put on recommendation-based systems to the online socialize networking systems proposed. Recommendation based systems for socialize networking contrast after distinctive classified recommendation resolutions, for they advocate humans to others relatively extinct properties. Collaborative filtering as recommender-based systems effectively implemented in various apps. Also, Social network-based approaches have been revealed to decrease the problems with cold start users. Here in this paper, we are going to discuss a model of recommender-based systems that consume available public socialize networks information, implements it with database for customize and personal recommendations and method of cold start problem.

References

  1. 1.
  2. 2.
    Zheng H, Wu J (2017) Friend recommendation in online social networks: perspective of social influence maximization. IEEEGoogle Scholar
  3. 3.
    Taneja A, Gupta P, Garg A (2016) Social graph based recommendation location using user’s behaviour. In: 2016 4th international conference on PGDCGoogle Scholar
  4. 4.
    Linden G, Smith B, York J (2003) Amazon.com recommendations: product-to-product collaborative filtering. IEEE Internet Computing Industry ReportGoogle Scholar
  5. 5.
    Aranda J, Givoni I, Handcock J, Tarlow D (2007) An online social network-based recommendation systemGoogle Scholar
  6. 6.
    Ng A, Duchi J (2012) CS229: Machine Learning. Lecture Notes. Stanford UniversityGoogle Scholar
  7. 7.
    Gupta A, Budania H, Singh P, Singh PK (2017) Facebook based choice filtering. In: 2017 IEEE 7th international advance computing conferenceGoogle Scholar
  8. 8.
    Lops P, de Gemmis M, Semeraro G (2011) Content-based recommender systems: state of the art and trends. In: Ricci F, Rokach L, Shapira B, Kantor PB (eds) Recommender systems handbook. Springer, Boston, pp 73–105Google Scholar
  9. 9.
    Recommender systems, Part 1: Introduction to approaches and algorithms. http://www.ibm.com/developerworks/library/os-recommender1/
  10. 10.
    Abbassi Z, Lakshmanan LVS (2009) On efficient recommendations for online exchange markets. In: Proceedings of the IEEE international conference on data engineering, ICDEGoogle Scholar
  11. 11.
    Roy SB, Thirumuruganathan S, Das G, Amer-Yahia S, Yu C (2014) Exploiting group recommendation functions for flexible preferences. In: Proceedings of the IEEE international conference on data engineering, ICDEGoogle Scholar
  12. 12.
    Kanagal B, Ahmed A, Pandey S, Josifovski V, Yuan J, Pueyo LG (2012) Supercharging recommender systems using taxonomies for learning user purchase behavior. In: Proceedings of the international conference on very large data bases, VLDB, vol 5, issue 10, pp 956–967CrossRefGoogle Scholar
  13. 13.
    Kailun H, Hsu W, Lee ML (2013) Utilizing social pressure in recommender systems. In: Proceedings of the IEEE international conference on data engineering, ICDEGoogle Scholar
  14. 14.
    Roy SB, Amer-Yahia S, Chawla A, Das G, Yu C (2010) Space efficiency in group recommendation. VLDB J 19(6):877–900CrossRefGoogle Scholar
  15. 15.
    Su H, Zheng K, Huang J, Jeung H, Chen L, Zhou X (2014) CrowdPlanner: a crowd-based route recommendation system. In: Proceedings of the IEEE international conference on data engineering, ICDEGoogle Scholar
  16. 16.
    Vartak M, Madden S (2013) CHIC: a combination-based recommendation system. In: Proceedings of the ACM international conference on management of data, SIGMODGoogle Scholar
  17. 17.
    Yin H, Cui B, Li J, Yao J, Chen C (2012) Challenging the long tail recommendation. Proc VLDB Endowment 5:896–907 CrossRefGoogle Scholar
  18. 18.
    Sarwat M, Moraffah R, Mokbel MF, Avery JL (2017) Database system support for personalized recommendation applications. In: 2017 IEEE 33rd international conference on data engineeringGoogle Scholar
  19. 19.
    Statista (2016) Global social networks ranked by number of users 2016. http://www.statista.com/statistics/272014/globalsocial-networks-ranked-by-number-of-users/ (Online)
  20. 20.
    De Macedo AQ, Marinho LB (2014) Event recommendation in event based social networks. In: HT (Doctoral Consortium/Late-breaking Results/Workshops)Google Scholar
  21. 21.
    Macedo AQ, Marinho LB, Santos RL (2015) Context-aware event recommendation in event-based social networks. In: Proceedings of the 9th ACM conference on recommender systems. ACM, pp 123–130Google Scholar
  22. 22.
    Basu C, Hirsh H, Cohen W et al (1998) Recommendation as classification: using social and content-based information in recommendation. In: AAAI/IAAI, pp 714–720Google Scholar
  23. 23.
    Daly EM, Geyer W (2011) Effective event discovery: using location and social information for scoping event recommendations. In: Proceedings of the fifth ACM conference on Recommender systems. ACM, pp 277–280Google Scholar
  24. 24.
    Caruana R, Niculescu-Mizil A, Crew G, Ksikes A (2004) Ensemble selection from libraries of models. In: Proceedings of the twenty-first international conference on Machine learning. ACM, p 18Google Scholar
  25. 25.
    netflix (2016) Netflix prize homepage. http://www.netflixprize.com (Online)
  26. 26.
    Sill J, Takács G, Mackey L, Lin D (2009) Feature-weighted linear stacking. arXiv preprint arXiv:0911.0460
  27. 27.
    Antolić G, Brkić L (2016) Recommender system based on the analysis of publicly available data. In: 2016 eighth international conference on knowledge and systems engineering (KSE)Google Scholar
  28. 28.
    Fleder D, Hosanagar K (2009) Blockbuster culture’s next rise or fall: the impact of recommender systems on sales diversity. Manage Sci 55(5):697–712CrossRefGoogle Scholar
  29. 29.
    Koutrika G, Bercovitz B, Garcia-Molina H (2009) FlexRecs: expressing and combining flexible recommendations. In: Proceedings of the ACM international conference on management of data, SIGMODGoogle Scholar
  30. 30.
    Parameswaran AG, Garcia-Molina H, Ullman JD (2010) Evaluating, combining and generalizing recommendations with prerequisites. In: Proceedings of the international conference on information and knowledge management, CIKMGoogle Scholar
  31. 31.
    Parameswaran AG, Venetis P, Garcia-Molina H (2011) Recommendation systems with complex constraints: a course recommendation perspective. ACM Trans Inf Syst TOIS 29(4):20Google Scholar
  32. 32.
    Adomavicius G, Tuzhilin A, Zheng R (2011) Request: a request language for customizing recommendations. Inf Syst Res 22(1):99–117CrossRefGoogle Scholar
  33. 33.
    Ekstrand MD, Ludwig M, Konstan JA, Riedl JT (2011) Rethinking the recommender research ecosystem: reproducibility, openness, and lenskit. In: Proceedings of the ACM conference on recommender systems, RecSYSGoogle Scholar
  34. 34.
    Levandoski JJ, Sarwat M, Mokbel MF, Ekstrand MD (2012) Rec-Store: an extensible and adaptive framework for online recommender queries inside the database engine. In: Proceedings of the international conference on extending database technology, EDBTGoogle Scholar
  35. 35.
    Chatzopoulou G, Eirinaki M, Koshy S, Mittal S, Polyzotis N, Varman JSV (2011) The queried system for personalized request recommendations. IEEE Data Eng Bull 34(2):55–60Google Scholar
  36. 36.
    Drosou M, Pitoura E (2013) YMALDB: a result-driven recommendation system for databases. In: Proceedings of the international conference on extending database technology, EDBTGoogle Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer SciencePrince Sattam Bin Abdul Aziz UniversityRiyadhKingdom of Saudi Arabia
  2. 2.Department of Computer ScienceSri Padmavati Mahila VisvavidyalayamTirupatiIndia

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