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Recommendation Systems: Bridging Technical Aspects with Marketing Implications

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Semantic Hyper/Multimedia Adaptation

Part of the book series: Studies in Computational Intelligence ((SCI,volume 418))

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Abstract

In 2010, Web users ordered, only in Amazon, 73 items per second and massively contribute reviews about their consuming experience. As the Web matures and becomes social and participatory, collaborative filters are the basic complement in searching online information about people, events and products.

In Web 2.0, what connected consumers create is not simply content (e.g. reviews) but context. This new contextual framework of consumption emerges through the aggregation and collaborative filtering of personal preferences about goods in the Web in massive scale. More importantly, facilitates connected consumers to search and navigate the complex Web more effectively and amplifies incentives for quality.

The objective of the present article is to jointly review the basic stylized facts of relevant research in recommendation systems in computer and marketing studies in order to share some common insights.

After providing a comprehensive definition of goods and Users in the Web, we describe a classification of recommendation systems based on two families of criteria: how recommendations are formed and input data availability. The classification is presented under a common minimal matrix notation and is used as a bridge to related issues in the business and marketing literature. We focus our analysis in the fields of one-to-one marketing, network-based marketing Web merchandising and atmospherics and their implications in the processes of personalization and adaptation in the Web. Market Basket Analysis is investigated in context of recommendation systems. Discussion on further research refers to the business implications and technological challenges of recommendation systems.

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References

  1. Albert, R., Barabási, A.: Statistical mechanics of complex networks. Reviews of Modern Physics 74(1), 47–97 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  2. Vafopoulos, M., Theodoridis, T., Kontokostas, D.: Inter-Viewing the Amazon Web Salespersons: Trends, Complementarities and Competition. In: 15th Panhellenic Conference on Informatics (PCI), pp. 299–303 (2011)

    Google Scholar 

  3. Anagnostopoulos, I., Stavropoulos, P.: On the feasibility of applying capture recapture experiments for web evolution estimations. Applied Mathematics Letters 24(6), 1031–1036 (2011)

    Article  Google Scholar 

  4. Hendler, J., Shadbolt, N., Hall, W., Berners-Lee, T., Weitzner, D.: Web science: an interdisciplinary approach to understanding the web. Communications of the ACM 51(7), 60–69 (2008)

    Article  Google Scholar 

  5. White, S., Vafopoulos, M.: Web Science: expanding the notion of Computer Science. In: 43rd ACM Technical Symposium on Computer Science Education (2011)

    Google Scholar 

  6. Vafopoulos, M.: Web Science Subject Categorization (WSSC). In: Proceedings of the ACM WebSci 2011, pp. 1–3 (2011)

    Google Scholar 

  7. Vafopoulos, M.: Being, space and time in the Web. Metaphilosophy (forthcoming, 2012)

    Google Scholar 

  8. Vafopoulos, M., Gravvanis, G., Platis, A.: The personal Grid e-workspace (g-work), Grid Techn., pp. 209–234. WIT Press (2006)

    Google Scholar 

  9. Vafopoulos, M.: A Roadmap to the Grid e-Workspace. In: Szczepaniak, P.S., Kacprzyk, J., Niewiadomski, A. (eds.) AWIC 2005. LNCS (LNAI), vol. 3528, pp. 504–509. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  10. Vafopoulos, M.: Information Society: the two faces of Janus. Artificial Intelligence Applications and Innovations, 643–648 (2006)

    Google Scholar 

  11. Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Recommender Systems Handbook, pp. 107–144 (2011)

    Google Scholar 

  12. Ellison, G., Ellison, S.F.: Lessons about Markets from the Internet. The Journal of Economic Perspectives 19(2), 139–158 (2005)

    Article  MathSciNet  Google Scholar 

  13. Brynjolfsson, E., Hu, Y., Smith, M.: Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers. Management Science 49(11), 1580–1596 (2003)

    Article  Google Scholar 

  14. Baye, M., Morgan, J., Scholten, P.: Information, search, and price dispersion. Economics and Information systems 1, 323–373 (2006)

    Article  Google Scholar 

  15. Benkler, Y.: The wealth of networks: How social production transforms markets and freedom. Yale University Press, New Haven (2006)

    Google Scholar 

  16. Ngai, E., Wat, F.: A literature review and classification of electronic commerce research. Information & Management 39(5), 415–429 (2002)

    Article  Google Scholar 

  17. Wang, C.C., Chen, C.C.: Electronic Commerce Research in Latest Decade: A Literature Review. International Journal of Electronic Commerce 1(1), 1–14 (2010)

    Google Scholar 

  18. Spulber, D.: Consumer Coordination in the Small and in the Large: Implications for Antitrust in Markets with Network Effects. Journal of Competition Law and Economics 4(2), 207 (2008)

    Article  Google Scholar 

  19. Vafopoulos, M., Theodoridis, T., Kontokostas, D.: Inter-Viewing the Amazon Web Salespersons: Trends, Complementarities and Competition. In: 15th Panhellenic Conference on Informatics (PCI), pp. 299–303 (2011)

    Google Scholar 

  20. Coase, R.: The Problem of Social Cost. Journal of Law & Economics 3, 1–44 (1960)

    Article  Google Scholar 

  21. Hayek, F.: Spontaneous (’grown’) order and organized (’made’) order. Markets, hierarchies and networks: the coordination of social life, 293 (1991)

    Google Scholar 

  22. Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge Univ. Pr., New York (2010)

    Google Scholar 

  23. Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. Knowledge Creation Diffusion Utilization 17(6), 734–749 (2005)

    Google Scholar 

  24. Vafopoulos, M.: Modeling the Web Economy: Web Users and Goods. In: WebSci 2011, Koblenz, Germany, June 14-17 (2011)

    Google Scholar 

  25. Hendler, J., Shadbolt, N., Hall, W., Berners-Lee, T., Weitzner, D.: Web science: an interdisciplinary approach to understanding the web. Communications of the ACM 51(7), 60–69 (2008)

    Article  Google Scholar 

  26. Berkovsky, S., Kuflik, T., Ricci, F.: Mediation of user models for enhanced personalization in recommender systems. User Modeling and User-Adapted Interaction 18(3), 245–286 (2007)

    Article  Google Scholar 

  27. Balabanović, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Communications of the ACM 40(3), 66–72 (1997)

    Article  Google Scholar 

  28. Morita, M., Shinoda, Y.: Information filtering based on user behavior analysis and best match text retrieval. In: Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 272–281 (1994)

    Google Scholar 

  29. Cacheda, F., Carneiro, V., Fernández, D., Formoso, V.: Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Transactions on the Web (TWEB) 5(1), 2 (2011)

    Google Scholar 

  30. Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)

    Article  MATH  Google Scholar 

  31. Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Transactions on Information Systems 22(1), 143–177 (2004)

    Article  Google Scholar 

  32. Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Advances in Artificial Intelligence 2009, 4 (2009)

    Article  Google Scholar 

  33. Kobsa, A., Koenemann, J.: Personalized Hypermedia Presentation Techniques for Improving Online Customer Relationships. The Knowledge Engineering Review 16(2), 111 (2000)

    Article  Google Scholar 

  34. Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 263–272 (2008)

    Google Scholar 

  35. Resnick, P.: Recommender systems. Communications of the ACM (1997)

    Google Scholar 

  36. Bodapati, A.V.: Recommendation Systems with Purchase Data. Journal of Marketing Research 45(1), 77–93 (2008)

    Article  Google Scholar 

  37. Samaan, N., Karmouch, A.: A mobility prediction architecture based on contextual knowledge and spatial conceptual maps. IEEE Transactions on Mobile Computing 4(6), 537–551 (2005)

    Article  Google Scholar 

  38. Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Recommender Systems Handbook, pp. 217–253 (2011)

    Google Scholar 

  39. McNee, S., Lam, S., Konstan, J.: Interfaces for Eliciting New User Preferences in Recommender Systems. In: Brusilovsky, P., Corbett, A.T., de Rosis, F. (eds.) UM 2003. LNCS (LNAI), vol. 2702, pp. 178–187. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  40. Chaffey, D., Ellis-Chadwick, F., Mayer, R., Johnston, K.: Internet Marketing Strategy Implementation and Practice. Prentice Hall (2009)

    Google Scholar 

  41. Hung, L.: A personalized recommendation system based on product taxonomy for one-to-one marketing online. Expert Systems with Applications 29(2), 383–392 (2005)

    Article  Google Scholar 

  42. Kim, W.: Personalization: Definition, status, and challenges ahead. Journal of Object Technology 1(1), 29–40 (2002)

    Article  Google Scholar 

  43. Pitta, D.: Marketing one-to-one and its dependence on knowledge discovery in databases. Journal of Consumer Marketing 15(5), 468–480 (1998)

    Article  Google Scholar 

  44. Gillenson, M., Sherrell, D.: Information Technology as the enabler of one-to-one marketing tutorial. Communications of the AIS 2 (September 1999)

    Google Scholar 

  45. Blom, J.: Personalization: a taxonomy. In: CHI 2000: Extended Abstracts on Human Factors in Computing Systems, pp. 313–314 (April 2000)

    Google Scholar 

  46. Cingil, I., Dogac, A., Azgin, A.: A broader approach to personalization. Communications of the ACM 43(8), 136–141 (2000)

    Article  Google Scholar 

  47. Germanakos, P., Mourlas, C.: Adaptation and Personalization of Web-based Multimedia Content. Media, 1–29 (2000)

    Google Scholar 

  48. Berman, B., Evans, J.R.: Retail management: a strategic approach. Prentice Hall, Upper Saddle River (1998)

    Google Scholar 

  49. Lee, J., Podlaseck, M., Schonberg, E., Hoch, R., Gomory, S.: Analysis and Visualization of Metrics for Online Merchandising. In: Masand, B., Spiliopoulou, M. (eds.) WebKDD 1999. LNCS (LNAI), vol. 1836, pp. 126–141. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  50. Lee, J., Podlaseck, M., Schonberg, E., Hoch, R.: Visualization and analysis of clickstream data of online stores for understanding web merchandising. Data Mining and Knowledge Discovery 5(1), 59–84 (2001)

    Article  Google Scholar 

  51. Machleit, K.A., Eroglu, S.A., Mantel, S.P.: Online retail atmospherics: empirical test of a cue typology. Journal of Consumer Psychology 9(1), 29–42 (2000)

    Article  Google Scholar 

  52. Manganari, E.E., Siomkos, G.J., Vrechopoulos, A.P.: Store atmosphere in web retailing. European Journal of Marketing 43(9/10), 1140–1153 (2009)

    Article  Google Scholar 

  53. Siomkos, G., Vrechopoulos, A., Manganari, E.: Web-Atmospheric Effects on Online Consumer Behavior: A Review of the Literature. In: Proceedings of IADIS International Conference e-Commerce, pp. 333–337 (2006)

    Google Scholar 

  54. Vrechopoulos, A., O’ Keefe, R., Doukidis, G., Siomkos, G.: Virtual store layout: an experimental comparison. Journal of Retailing (2004)

    Google Scholar 

  55. Nanou, T., Lekakos, G., Fouskas, K.: The effects of recommendations’ presentation on persuasion and satisfaction in a movie recommender system. Multimedia Systems 16, 219–230 (2010)

    Article  Google Scholar 

  56. Mild, A., Reutterer, T.: An improved collaborative filtering approach for predicting cross-category purchases based on binary market basket data. Journal of Retailing and Consumer Services 10(3), 123–133 (2003)

    Article  Google Scholar 

  57. Boztug, Y., Hildebrandt, L.: A market basket analysis conducted with a multivariate logit model. SFB 649 Discussion Papers (2005)

    Google Scholar 

  58. Aggarwal, C.C., Procopiuc, C., Yu, P.S.: Finding localized associations in market basket data. IEEE Transactions on Knowledge and Data Engineering 14(1), 51–62 (2002)

    Article  Google Scholar 

  59. Chen, Y., Tang, K., Shen, R., Hu, Y.: Market basket analysis in a multiple store environment. Decision Support Systems 40(2), 339–354 (2005)

    Article  Google Scholar 

  60. Chib, S., Seetharaman, P., Strijnev, A.: Analysis of multi-category purchase incidence decisions using IRI market basket data. Advances in Econometrics 16, 57–92 (2002)

    Article  Google Scholar 

  61. Chiang, J.: A simultaneous approach to the whether, what and how much to buy questions. Marketing Science 10(4), 297–315 (1991)

    Article  Google Scholar 

  62. Aggarwal, C.C., Wolf, J.L., Yu, P.S.: A new method for similarity indexing of market basket data, vol. 28(2), pp. 407–418. ACM, New York (1999)

    Google Scholar 

  63. Hao, M., Dayal, U., Hsu, M.: Visualization of directed associations in e-commerce transaction data. Data Visualization (2001)

    Google Scholar 

  64. Kantardzic, M.: Data mining: concepts, models, methods, and algorithms. Wiley-Interscience (2003)

    Google Scholar 

  65. Oestreicher-Singer, G., Sundararajan, A.: Recommendation Networks and the long tail of electronic commerce. Working Paper (2009)

    Google Scholar 

  66. Krebs, V.: The Social Life of Books: Visualizing Communities of Interest via Purchase Patterns on the WWW (1999)

    Google Scholar 

  67. Dhar, V., Oestreicher-Singer, G., Sundararajan, A., Umyarov, A.: The gestalt in graphs: Prediction using economic networks. NYU Working Paper No. CEDER-09-06 (2009)

    Google Scholar 

  68. Oestreicher-Singer, G., Sundararajan, A.: Network Structure and the Long Tail of ECommerce Demand. In: Proc. 27th Internat. Conf. Inform. Systems, Milwaukee WI (2006)

    Google Scholar 

  69. Oestreicher-Singer, G., Sundararajan, A.: Linking Network Structure to Ecommerce Demand: Theory and Evidence from Amazon.com’s Copurchase Network. In: 34th Telecommunications Policy Research Conference, pp. 1–14 (2007)

    Google Scholar 

  70. Yitzhaki, S.: Relative deprivation and the Gini coefficient. The Quarterly Journal of Economics 93(2), 321–324 (1979)

    Article  Google Scholar 

  71. Anderson, C.: The long tail. Hyperion, New York (2006)

    Google Scholar 

  72. Oestreicher-singer, G., Sundararajan, A.: The Visible Hand of Social Networks in Electronic Markets. Electronic Commerce Research (2008)

    Google Scholar 

  73. Carmi, E., Oestreicher-Singer, G., Sundararajan, A.: Spreading the Oprah Effect: The Diffusion of Demand Shocks in a Recommendation Network. In: ICIS 2009 Proceedings, p. 78 (2009)

    Google Scholar 

  74. Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U.: Complex networks: Structure and dynamics. Physics Reports 424(4-5), 175–308 (2006)

    Article  MathSciNet  Google Scholar 

  75. Reichardt, J., Bornholdt, S.: Statistical mechanics of community detection. Physical Review E 74(1), 016110 (2006)

    Google Scholar 

  76. Hill, S., Provost, F., Volinsky, C.: Network-Based Marketing: Identifying Likely Adopters via Consumer Networks. Statistical Science 21(2), 256–276 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  77. Rosen, E.: The anatomy of buzz: How to create word-of-mouth marketing. Crown Business (2002)

    Google Scholar 

  78. Leskovec, J., Adamic, L., Huberman, B.: The dynamics of viral marketing. ACM Transactions on the Web 1(1), 5-es (2007)

    Google Scholar 

  79. Dellarocas, C.: The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management Science, 1407–1424 (2003)

    Google Scholar 

  80. Dellarocas, C.: Efficiency and robustness of binary feedback mechanisms in trading environments with moral hazard.MIT Sloan School of Management Working Paper no. 4297-03 (2003)

    Google Scholar 

  81. Getoor, L., Taskar, B.: Introduction to statistical relational learning. The MIT Press (2007)

    Google Scholar 

  82. Clemons, E.K., Madhani, N.: Regulation of Digital Businesses with Natural Monopolies or Third-Party Payment Business Models: Antitrust Lessons from the Analysis of Google. Journal of Management Information Systems 27(3), 43–80 (2010)

    Article  Google Scholar 

  83. Hepp, M.: Goodrelations: An ontology for describing products and services offers on the web. Knowledge Engineering: Practice and Patterns, 329–346 (2008)

    Google Scholar 

  84. Boley, H., Tabet, S., Wagner, G.: Design rationale of RuleML: A markup language for semantic web rules. In: International Semantic Web Working Symposium (SWWS), pp. 381–402 (2001)

    Google Scholar 

  85. Weitzner, D., Abelson, H., Berners-Lee, T., Feigenbaum, J., Hendler, J., Sussman, G.J.: Information accountability. Communications of the ACM 51(6), 82–87 (2008)

    Article  Google Scholar 

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Authors and Affiliations

  1. Mathematics Department, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Vafopoulos Michalis & Oikonomou Michael

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  1. Vafopoulos Michalis
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  2. Oikonomou Michael
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Editors and Affiliations

  1. , Department of Computer Science, University of Central Greece, Lamia, 35100, Greece

    Ioannis E. Anagnostopoulos

  2. in Bratislava, Institute of Informatics, Slovak University of Technology, Ilkovičova 3, Bratislava, 842 16, Slovakia

    Mária Bieliková

  3. Department of Informatics, Ionian University, Tsirigoti Square 7, Corfu, 49100, Greece

    Phivos Mylonas

  4. , Dept. of Communication and Internet Stud, Cyprus University of Technology, 31 Archbishop Kyprianos Str., Lemesos, 3036, Cyprus

    Nicolas Tsapatsoulis

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Michalis, V., Michael, O. (2013). Recommendation Systems: Bridging Technical Aspects with Marketing Implications. In: Anagnostopoulos, I., Bieliková, M., Mylonas, P., Tsapatsoulis, N. (eds) Semantic Hyper/Multimedia Adaptation. Studies in Computational Intelligence, vol 418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28977-4_5

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  • DOI: https://doi.org/10.1007/978-3-642-28977-4_5

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