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Recommender Systems: Network Approaches

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Multimedia Services in Intelligent Environments

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 24))

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Abstract

The use of recommender systems is now common across the Web as users are guided to items of interest using prediction models based on known user data. In essence the user is shielded from information overload by being presented solely with data relevant to that user. Whilst this process is transparent, for the user, it transfers the burden of data analysis to an automated system that is required to produce meaningful results in real time from a huge amount of information. Traditionally structured data has been stored in relational databases to enable access and analysis. This chapter proposes the investigation of a new approach, to efficiently handle the extreme levels of information, based on a network of linked data. This aligns with more up-to-date methods, currently experiencing a surge of interest, loosely termed NoSQL databases. By forsaking an adherence to the relational model it is possible to efficiently store and reason over huge collections of unstructured data such as user data, document files, multimedia objects, communications, email and social networks. It is proposed to represent users and preferences as a complex network of vertices and edges. This allows the use of many graph-based measures and techniques by which relevant information and the underlying topology of the user structures can be quickly and accurately obtained. The centrality of a user, based on betweenness or closeness, is investigated using the Eigenvalues of the Laplacian spectrum of the generated graph. This provides a compact model of the data set and a measure of the relevance or importance of a particular vertex. Newly-developed techniques are assessed using Active Clustering and Acquaintance Nomination to identify the most influential participants in the network and so provide the best recommendations to general users based on a sample of their identified exemplars. Finally an implementation of the system is evaluated using real-world data.

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References

  1. Adomavicius, G., Tuzhilin, A.: Extending recommender systems: a multidimensional approach. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-01), Workshop on Intelligent Techniques for Web Personalization (ITWP2001), Seattle, Washington (2001)

    Google Scholar 

  2. Aggarwal, C.C., Wolf, J.L., Wu, K.-L., Yu, P.S.: Horting hatches an egg: a new graph-theoretic approach to collaborative filtering. In: Proceedings of the Fifth ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’99), pp. 201–212, San Diego (1999)

    Google Scholar 

  3. Balabanovic, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)

    Article  Google Scholar 

  4. Berry, M.J.A., Linoff, J.S.: Data mining techniques for marketing, sales and customer relationship management, 3rd edn, Wiley, Indiana (2004)

    Google Scholar 

  5. Boutilier C., Reiter, R. Price, R.: Symbolic dynamic programming for first-order MDPs. In: Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), Seattle, pp. 690–697 (2001)

    Google Scholar 

  6. Cacheda F., Carneiro, V., Fernández, D., Formoso, V.: Improving k-nearest neighbors algorithms: practical application of dataset analysis. In: Berendt, B., de Vries, A., Fan, W., Macdonald, C., Ounis, I., Ruthven, I. (eds.) Proceedings of the 20th ACM international conference on Information and knowledge management (CIKM ‘11), ACM, New York (2011)

    Google Scholar 

  7. Cai, D., He, X., Wen, J.-R., Ma, W.-Y.: Block-level link analysis. In: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 440–447, ACM, (2004)

    Google Scholar 

  8. Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining content-based and collaborative filters in an online newspaper. In: Proceedings of the ACM SIGIR’99 workshop on recommender systems, (1999)

    Google Scholar 

  9. Di Nitto, E., Dubois, D.J., Mirandola, R., Saffre, F., Tateson, R.: Applying self-aggregation to load balancing: experimental results. In: Proceedings of the 3rd international Conference on Bio-inspired Models of Network, Information and Computing Systems (Bionetics 2008), Article 14, 25–28 November 2008

    Google Scholar 

  10. Golbandi N., Koren, Y., Lempel, R. (2011) Adaptive bootstrapping of recommender systems using decision trees. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining (WSDM ‘11). ACM, New York, NY, USA

    Google Scholar 

  11. Goldberg, D., Nichols, D., Oki, B., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)

    Article  Google Scholar 

  12. Guestrin, C., Koller, D., Parr, R., Venkataraman, S.: Efficient solution algorithms for factored MDPs. J. Artif. Intell. Res. 19, 399–468 (2003)

    MathSciNet  MATH  Google Scholar 

  13. Han J., Haihong, E., Le, G., Du, J.: Survey on NoSQL database. In: Proceedings of the 6th International Conference on Pervasive Computing and Applications (ICPCA), 2011, pp. 363–366, (2011)

    Google Scholar 

  14. Huang, Z., Chung, W., Chen, H.: A graph model for E-commerce recommender systems. J. Am. Soc. Inf. Sci. 55, 259–274 (2004)

    Article  Google Scholar 

  15. Jamakovic, A., Van Mieghem, P.: On the robustness of complex networks by using the algebraic connectivity. In: Das et al A. (ed.) NETWORKING 2008 Ad Hoc and sensor networks, wireless networks, next generation internet, pp. 183–194, LNCS 4982, Springer (2008)

    Google Scholar 

  16. Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender systems: An introduction. Cambridge University Press, Cambridge (2011)

    Google Scholar 

  17. Leavitt, N.: Will NoSQL Databases Live Up to Their Promise? Computer 43(2), 12–14 (2010)

    Article  Google Scholar 

  18. Lihua, W., Lu, L., Jing, L., Zongyong, L.: Modeling user multiple interests by an improved GCS approach. Expert Syst. Appl. 29, 757–767 (2005)

    Article  Google Scholar 

  19. Lopez-Nores, M., Garca-Duque, J., Frenandez-Vilas, R.P., Bermejo-Munoz, J.: A flexible semantic inference methodology to reason about user preference in knowledge-based recommender systems. Knowl.-Based Syst. 21, 305–320 (2008)

    Article  Google Scholar 

  20. Meng-Ju, H., Chao-Rui, C., Li-Yung, H., Jan–Jan, W., Pangfeng, L.: SQLMR : A scalable Database management system for cloud computing. In: Proceedings of the International Conference on Parallel Processing (ICPP), 2011, pp. 315–324, (2011)

    Google Scholar 

  21. Mirza, B.J.: Jumping connections: A graph-theoretic model for recommender systems. MSc. Dissertation, Faculty of the Virginia Polytechnic Institute and State University. http://scholar.lib.vt.edu/theses/available/etd-02282001-175040/unrestricted/etd.pdf. Accessed 13 July 2012 (2001)

  22. Mooney, R., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the Fifth ACM Conference on Digital Libraries, pp: 195–204, San Antonio, (2000)

    Google Scholar 

  23. Price R., Boutilier, C.: Imitation and reinforcement learning in agents with heterogeneous actions. In: Proceedings of the AISB’00 Symposium on Starting from Society—the Application of Social Analogies to Computational Systems, pp: 85–92, Birmingham, (2000)

    Google Scholar 

  24. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM conference on Computer supported cooperative work (CSCW’94), pp. 175–186. ACM, New York, (1994)

    Google Scholar 

  25. Sarwar B., Karypis, G., Konstan, J.A., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: Proceedings of the ACM E-Commerce, pp. 158–167, (2000)

    Google Scholar 

  26. Schafer J.B., Konstan, J., Riedl, J.: Recommender Systems in E-Commerce. In: ACM Conference on Electronic Commerce, 1999, pp. 158–166

    Google Scholar 

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Authors and Affiliations

  1. School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK

    David Lamb, Martin Randles & Dhiya Al-Jumeily

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  1. David Lamb
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  2. Martin Randles
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  3. Dhiya Al-Jumeily
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Correspondence to David Lamb .

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Editors and Affiliations

  1. , Department of Informatics, University of Piraeus, Karaoli&Dimitriou St. 80, Piraeus, 18534, Greece

    George A. Tsihrintzis

  2. , Department of Informatics, University of Piraeus, Karaoli&Dimitriou St. 80, Piraeus, 18534, Greece

    Maria Virvou

  3. , School of Electrical and, University of South Australia, Mawson Lakes Campus, Adelaide, 5095, South Australia, Australia

    Lakhmi C. Jain

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Lamb, D., Randles, M., Al-Jumeily, D. (2013). Recommender Systems: Network Approaches. In: Tsihrintzis, G., Virvou, M., Jain, L. (eds) Multimedia Services in Intelligent Environments. Smart Innovation, Systems and Technologies, vol 24. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00372-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-00372-6_4

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  • Print ISBN: 978-3-319-00371-9

  • Online ISBN: 978-3-319-00372-6

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