Abstract
There is an increasing trend amongst users to consume information from websites and social media. With the huge influx of content it becomes challenging for the consumers to navigate to topics or articles that interest them. Particularly in health care, the content consumed by a user is controlled by various factors such as demographics and lifestyle. In this paper, we use a semi-bipartite network model to capture the interactions between users and health topics that interest them. We use a supervised link prediction approach to recommend topics to users based on their past reading behavior and contextual data associated to a user such as demographics.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
References
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)
Backstrom, L., Leskovec, J., Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM 2011, pp. 635–644. ACM, New York, NY, USA (2011)
Davis, D., Lichtenwalter, R., Chawla, N.V.: Multi-relational link prediction in heterogeneous information networks. In: Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011, pp. 281–288. IEEE Computer Society, Washington, DC, USA (2011)
Xu, K., Williams, R., Hong, S.-H., Liu, Q., Zhang, J.: Semi-bipartite graph visualization for gene ontology networks. In: Eppstein, D., Gansner, E.R. (eds.) GD 2009. LNCS, vol. 5849, pp. 244–255. Springer, Heidelberg (2010)
Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25, 211–230 (2001)
Barabasi, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web (1999)
Benchettara, N., Kanawati, R., Rouveirol, C.: Supervised machine learning applied to link prediction in bipartite social networks. In: Memon, N., Alhajj, R. (eds.) ASONAM, pp. 326–330. IEEE Computer Society (2010)
Kunegis, J., De Luca, E.W., Albayrak, S.: The link prediction problem in bipartite networks. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds.) IPMU 2010. LNCS, vol. 6178, pp. 380–389. Springer, Heidelberg (2010)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Hosmer, D.W., Lemeshow, S.: Applied Logistic Regression (Wiley Series in Probability and Statistics), 2nd edn. Wiley-Interscience Publication, New York (2000)
Bradley, A.P.: The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145–1159 (1997)
Acknowledgements
We would like to thank Everyday Health for providing us their user data.
This research was supported in part by National Science Foundation (NSF) Grant OCI-1029584 and IIS-1447795.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nigam, A., Chawla, N.V. (2016). Link Prediction in a Semi-bipartite Network for Recommendation. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9622. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49390-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-662-49390-8_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-49389-2
Online ISBN: 978-3-662-49390-8
eBook Packages: Computer ScienceComputer Science (R0)