Recommendation information diffusion in social networks considering user influence and semantics

  • Dionisis Margaris
  • Costas Vassilakis
  • Panagiotis Georgiadis
Original Article

Abstract

One of the major problems in the domain of social networks is the handling and diffusion of the vast, dynamic and disparate information created by its users. In this context, the information contributed by users can be exploited to generate recommendations for other users. Relevant recommender systems take into account static data from users’ profiles, such as location, age or gender, complemented with dynamic aspects stemming from the user behavior and/or social network state such as user preferences, items’ general acceptance and influence from social friends. In this paper, we enhance recommendation algorithms used in social networks by taking into account qualitative aspects of the recommended items, such as price and reliability, the influencing factors between social network users, the social network user behavior regarding their purchases in different item categories and the semantic categorization of the products to be recommended. The inclusion of these aspects leads to more accurate recommendations and diffusion of better user-targeted information. This allows for better exploitation of the limited recommendation space, and therefore, online advertisement efficiency is raised.

Keywords

Information diffusion Social networks Collaborative filtering Quality of service Semantic information 

References

  1. Amazon (2015) Amazon Product Advertising API. https://affiliate-program.amazon.com/gp/advertising/api/detail/main.html Accessed 17 Jan 17 2016
  2. Anagnostopoulos A, Kumar R, Mahdian M (2008) Influence and correlation in social networks. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (KDD ‘08), pp 7–15Google Scholar
  3. Androutsos D, Plataniotis KN, Venetsanopoulos AN (1998) Distance measures for color image retrieval. In: Proceedings of the international conference on image processing, vol 2, pp 770–774Google Scholar
  4. Arazy O, Kumar N, Shapira B (2009) Improving social recommender systems. IT professionalGoogle Scholar
  5. Aslam J, Montague M (2001) Models for metasearch. In: Croft WB, Harper DJ, Kraft DH, Zobel J (eds) Proceedings of the 24th annual international ACM SIGIR conference on research and development in information retrieval, SIGIR 2001, pp 276–284Google Scholar
  6. Bakshy E, Rosenn I, Marlow C, Adamic L (2012a) The role of social networks in information diffusion. In: Proceedings of the 21st international conference on World Wide Web, pp 519–528Google Scholar
  7. Bakshy E, Eckles D, Yan R, Rosenn I (2012b) Social influence in social advertising: evidence from field experiments. In: Proceedings of the 13th ACM conference on electronic commerce, pp 146–161Google Scholar
  8. Balabanovic M, Shoham Y (1997) Fab: content-based, collaborative recommendation. Commun ACM 40(3):66–72CrossRefGoogle Scholar
  9. Bizer C, Schultz A (2009) The Berlin Sparql benchmark. Int J Semant Web Inf Syst 1–24Google Scholar
  10. Bizer C, Schultz A (2010) Berlin SPARQL benchmark (BSBM): dataset specification. http://wifo5-03.informatik.uni-mannheim.de/bizer/berlinsparqlbenchmark/spec/Dataset/index.html. Accessed 16 Dec 2015
  11. Boulkrinat S, Hadjali A, Mokhtari A (2013) Enhancing recommender systems prediction through qualitative preference relations. In: 11th international symposium on programming and systems (ISPS), pp 74–80Google Scholar
  12. Cai X, Bain M, Krzywicki A, Wobcke W, Sok Kim Y, Compton P, Mahidadia A (2011) Collaborative filtering for people to people recommendation in social networks. Adv Artif Intell 6464:476–485MathSciNetGoogle Scholar
  13. Car reliability index (2016) http://www.reliabilityindex.com/. Accessed 15 Jan 2016
  14. Cardoso J (2002) Quality of service and semantic composition of workflows. PhD Thesis, Univ. of GeorgiaGoogle Scholar
  15. Chedrawy Z, Abidi SSR (2009) A web recommender system for recommending, predicting and personalizing music playlists. In: Proceedings of web information systems engineering (WISE 2009), pp 335–342Google Scholar
  16. Corder GW, Foreman DI (2014) Nonparametric statistics: a step-by-step approach. Wiley, New York. ISBN 978-1118840313MATHGoogle Scholar
  17. Data Center Knowledge (2012) The Facebook Data Center FAQ. http://www.datacenterknowledge.com/the-facebook-data-center-faq/. Accessed 8 Jan 2016
  18. Digicamhelp (2010) Most reliable brand digital cameras. http://www.digicamhelp.com/buying-guide/checklist/most-reliable-brand-digital-cameras/. Accessed 15 Jan 2016
  19. Facebook (2015a) Facebook home page. https://www.facebook.com
  20. Facebook (2015b). Facebook ad targeting. https://www.facebook.com/business/products/ads/ad-targeting. Accessed 12 Jan 2016
  21. Ganski RA, Wong H (1987) Optimization of nested SQL queries revisited. In: Proceedings of the 1987 ACM SIGMOD international conference on management of data, pp 22–33Google Scholar
  22. Ge M, Delgado-Battenfeld C, Jannach D (2010) Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: Proceedings of the fourth ACM conference on recommender systems (RecSys ‘10), pp 257–260Google Scholar
  23. Gilbert E, Karahalios K (2009) Predicting tie strength with social media. In: Proceedings of the SIGCHI conference on human factors in computing systems (CHI ‘09), pp 211–220Google Scholar
  24. Google Inc. (2015a) Google products and services taxonomy. https://developers.google.com/adwords/api/docs/appendix/productsservices. Accessed 11 Jan 2016
  25. Google Inc. (2015b) Control your Google ad. https://www.google.com/settings/u/0/ads/authenticated (note: user must sign into Google to view own settings). Accessed 17 Jan 2016
  26. Guille A, Hacid H, Favre C, Zighed DA (2013) Information diffusion in online social networks: a survey. SIGMOD Record 42(2)Google Scholar
  27. Hau J, Lee W, Darlington J (2005) A semantic similarity measure for semantic web services. In: Proceedings of WWW2005, May 10–14, 2005, Chiba, JapanGoogle Scholar
  28. He J, Chu WW (2010) A social network-based recommender system (SNRS). Ann Inf Syst 12:47–74CrossRefGoogle Scholar
  29. He D, Wu D (2008) Toward a robust data fusion for document retrieval. In: IEEE 4th international conference on natural language processing and knowledge engineering - NLP-KEGoogle Scholar
  30. Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53CrossRefGoogle Scholar
  31. HSQLDB (2015) http://hsqldb.org/. Accessed 25 Jan 2016
  32. Hwang CL, Yoon K (1981) Multiple criteria decision making, Lecture Notes in Economics and Mathematical Systems. Springer, BerlinGoogle Scholar
  33. ITU (1988) Recommendation E.800 Quality of service and dependability vocabularyGoogle Scholar
  34. Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the fourth ACM conference on recommender systems, RecSys 2010, Barcelona, SpainGoogle Scholar
  35. Karaiskos C (2013) Enhanced ontological searching of medical scientific information. Master’s Thesis, University of ManchesterGoogle Scholar
  36. Kim W (1982) On optimizing an SQL-like nested query. ACM Trans Database Syst 7(3):443–469CrossRefMATHGoogle Scholar
  37. Konstas I, Stathopoulos V, Jose JM (2009) On social networks and collaborative recommendation. In: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval, Boston, USAGoogle Scholar
  38. Kruskal W, Wallis W (1952) Use of Ranks in One-Criterion Variance Analysis. J Am Stat Asso 47(260):583CrossRefMATHGoogle Scholar
  39. Li W, Ye Z, Jin Q (2014) An integrated recommendation approach based on influence and trust in social networks. Future Inf Technol Lect Notes Electr Eng 309:83–89CrossRefGoogle Scholar
  40. Lipton ZC, Elkan C, Naryanaswamy B (2014) Optimal thresholding of classifiers to maximize F1 measure. In: Proceedings of ECML PKDD 2014 (part II), pp 225–239Google Scholar
  41. Liu S, Jianga C, Linb Z, Dinga Y, Duana R, Xu Z (2015) Identifying effective influencers based on trust for electronic word-of-mouth marketing: a domain-aware approach. Inf Sci 306:34–52CrossRefGoogle Scholar
  42. Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval, 1st edn. Cambridge University Press, Cambridge. ISBN 0521865719CrossRefMATHGoogle Scholar
  43. Margaris D, Georgiadis P, Vassilakis C (2013) Adapting WS-BPEL scenario execution using collaborative filtering techniques. In: Proceedings of the IEEE 7th RCIS conference, Paris, FranceGoogle Scholar
  44. Margaris D, Georgiadis P, Vassilakis C (2015a) A collaborative filtering algorithm with clustering for personalized web service selection in business processes. In: Proceedings of the IEEE 9th RCIS conference, Athens, GreeceGoogle Scholar
  45. Margaris D, Vassilakis C, Georgiadis P (2015b) An integrated framework for adapting WS-BPEL scenario execution using QoS and collaborative filtering techniques. Sci Comput Program 98:707–734CrossRefGoogle Scholar
  46. Masroor A (2015) Is social media the biggest influencer of buying decisions? http://www.socialmediatoday.com/marketing/masroor/2015-05-28/social-media-biggest-influencer-buying-decisions. Accessed 16 Jan 2016
  47. MySQL (2015) http://www.mysql.com/. Accessed 14 Dec 2015
  48. O’Sullivan J, Edmond D, Ter Hofstede A (2002) What is a service? Towards accurate description of non-functional properties. Distrib Parallel Databases 12Google Scholar
  49. Oechslein O, Hess T (2014) The value of a recommendation: the role of social ties in social recommender systems. In: 47th Hawaii international conference on system science, 2014, pp 1864–1873Google Scholar
  50. Olenski S (2012) Are brands wielding more influence in social media than we thought? http://www.forbes.com/sites/marketshare/2012/05/07/are-brands-wielding-more-influence-in-social-media-than-we-thought. Accessed 16 Jan 2016
  51. Pirasteh P, Jung JJ, Hwang D (2014) Item-based collaborative filtering with attribute correlation: a case study on movie recommendation. In: 6th Asian conference, ACIIDS 2014, Bangkok, Thailand, April 7–9, 2014, Proceedings, Part II, pp 245–252Google Scholar
  52. Quijano-Sanchez L, Recio-Garcia JA, Diaz-Agudo B (2011) Group recommendation methods for social network environments. In: 3rd workshop on recommender systems and the social web within the 5th ACM international conference on recommender systems (RecSys’11)Google Scholar
  53. Schafer JB, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems. In: “The Adaptive Web”, Lecture Notes in Computer Science, vol 4321, pp 291–324Google Scholar
  54. Schedl M, Knees P, Pohle T, Widmer G (2008) Towards an automatically generated music information system via web content mining. In: Proceedings of the 30th European conference on information retrieval (ECIR’08), Glasgow, Scotland, pp 585–590Google Scholar
  55. Shuiguang D, Longtao H, Xu G (2014) Social network-based service recommendation with trust enhancement. Expert Syst Appl 41(18):8075–8084CrossRefGoogle Scholar
  56. Sprout Social (2011) Social networks influence 74% of consumers’ buying decisions. http://sproutsocial.com/insights/social-networks-influence-buying-decisions/. Accessed 16 Jan 2016
  57. Squaretrade (2009) Laptop & Netbook reliability. https://www.squaretrade.com/htm/pdf/SquareTrade_laptop_reliability_1109.pdf. Accessed 15 Jan 2016
  58. Squaretrade (2010) SmartPhone reliability. http://www.squaretrade.com/cell-phone-comparison-study-nov-10. Accessed 15 Jan 2016
  59. Twitter (2015) Twitter home page. https://twitter.com/. Accessed 15 Dec 2015
  60. Ver Steeg G, Galstyan A (2012) Information transfer in social media. In: Proceedings of WWW 2012, April 16–20, 2012, Lyon, France, pp 509–518Google Scholar
  61. Walter F (2011) Trust as the basis of coalition formation in electronic marketplaces. Adv Complex Syst 14(02):111–131MathSciNetCrossRefGoogle Scholar
  62. Walter F, Battiston S, Yildirim M, Schweitzer F (2012) Moving recommender systems from on-line commerce to retail stores. Inf Syst e-Bus Manag 10(3):367–393CrossRefGoogle Scholar
  63. Wang F-K, Huang C-I, Chu T-P (2013) Reliability analysis of smartphones based on the field return data. In: Proceedings of the Institute of Industrial Engineers Asian Conference 2013, pp 1495–1502Google Scholar
  64. Wang Z, Liao J, Cao Q, Qi H, Wang Z (2015) Friendbook: a semantic-based friend recommendation system for social networks. IEEE Trans Mob Comput 14(3):538–551CrossRefGoogle Scholar
  65. Whitman B, Lawrence S (2002) Inferring descriptions and similarity for music from community metadata. In: Proceedings of the 2002 international computer music conference (Goteborg, Sweden), pp 591–598Google Scholar
  66. Zhang W, Chen T, Wang J, Yu Y (2013) Optimizing top-n collaborative filtering via dynamic negative item sampling. In: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval (SIGIR’13), pp 785–788Google Scholar

Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  1. 1.Department of Informatics and TelecommunicationsUniversity of AthensAthensGreece
  2. 2.Department of Informatics and TelecommunicationsUniversity of the PeloponneseTripoliGreece

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