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Network-Based Information Filtering Algorithms: Ranking and Recommendation

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Dynamics On and Of Complex Networks, Volume 2

Abstract

This chapter gives an overview of applications of random walks to information filtering, focusing on the tasks of ranking and recommendation in particular. Despite the amount of work done in these two directions, multiple important research challenges still remain open. Due to the massive amounts of available data, scalability of algorithms is of critical importance. Even when full computation is possible, one can think of potential approaches to update the output gradually when new data arrives. To achieve that, one can use or learn from perturbation theory which is a well-known tool in physics. It has been seen that results based on random walks often correlate strongly with mere popularity (represented by degree) of nodes in the network. Such bias toward popularity may be beneficial for an algorithm’s accuracy but it may also narrow one’s view of the given system and perhaps create a self reinforcing loop further boosting popularity of already popular nodes. Thus it is needed that information filtering algorithms converge less to the center of the given network. Random walks biased by node centrality or time information about nodes and links could provide a solution to this problem. As a beneficial side effect, this line of research could yield algorithms pointing us to fresh and promising content instead of highlighting old victors over and over again.

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Acknowledgement

This work was partially supported by the Swiss National Science Foundation Grant No. 200020-132253. I wish to thank a number of wonderful friends and colleagues who helped to shape many of the ideas presented here.

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Authors and Affiliations

  1. Physics Department, University of Fribourg, CH-1700, Fribourg, Switzerland

    Matúš Medo

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  1. Matúš Medo
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Correspondence to Matúš Medo .

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Editors and Affiliations

  1. Dept. Computer Science & Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India

    Animesh Mukherjee

  2. Microsoft Research Lab India, 9 Lavelle Road, Bangalore, 560001, India

    Monojit Choudhury

  3. , Laboratoire J.A. Dieudonné, Université de Nice - Sophie Antipolis, Parc Valrose, Nice 02, 06108, France

    Fernando Peruani

  4. Dept. Computer Science & Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India

    Niloy Ganguly

  5. Department of Mathematical Engineering, Université Catholique de Louvain, 4, Avenue George Lemaître, Louvain-la-Neuve, B-1348, Belgium

    Bivas Mitra

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Medo, M. (2013). Network-Based Information Filtering Algorithms: Ranking and Recommendation. In: Mukherjee, A., Choudhury, M., Peruani, F., Ganguly, N., Mitra, B. (eds) Dynamics On and Of Complex Networks, Volume 2. Modeling and Simulation in Science, Engineering and Technology. Birkhäuser, New York, NY. https://doi.org/10.1007/978-1-4614-6729-8_16

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