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.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
Balabanovic, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)
Berry, M.J.A., Linoff, J.S.: Data mining techniques for marketing, sales and customer relationship management, 3rd edn, Wiley, Indiana (2004)
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)
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)
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)
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)
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
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
Goldberg, D., Nichols, D., Oki, B., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)
Guestrin, C., Koller, D., Parr, R., Venkataraman, S.: Efficient solution algorithms for factored MDPs. J. Artif. Intell. Res. 19, 399–468 (2003)
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)
Huang, Z., Chung, W., Chen, H.: A graph model for E-commerce recommender systems. J. Am. Soc. Inf. Sci. 55, 259–274 (2004)
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)
Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender systems: An introduction. Cambridge University Press, Cambridge (2011)
Leavitt, N.: Will NoSQL Databases Live Up to Their Promise? Computer 43(2), 12–14 (2010)
Lihua, W., Lu, L., Jing, L., Zongyong, L.: Modeling user multiple interests by an improved GCS approach. Expert Syst. Appl. 29, 757–767 (2005)
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)
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)
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)
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)
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)
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)
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)
Schafer J.B., Konstan, J., Riedl, J.: Recommender Systems in E-Commerce. In: ACM Conference on Electronic Commerce, 1999, pp. 158–166
Resource List
This section contains a short list of related items useful for readers wishing to investigate further or learn more about the issues raised in this chapter:-
(Graph Theory tutorials)
http://homepages.cae.wisc.edu/~gautamd/Active_Clustering/Home.html
(Basic overview of Active Clustering in Graph Theory)
(Overview of NoSQL)
(Repository of NoSQL, key/value, and graph databases)
(“Alkindi” —Java Source code for commercial collaborative filtering recommender system, now unrestricted and no longer maintained)
(Mahout—Machine Learning Library, incorporating Recommender components. Part of the Apache project; maintained at the time of writing)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-00372-6_4
Published:
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00371-9
Online ISBN: 978-3-319-00372-6
eBook Packages: EngineeringEngineering (R0)