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Studying Recommendation Algorithms by Graph Analysis

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Abstract

We present a novel framework for studying recommendation algorithms in terms of the 'jumps' that they make to connect people to artifacts. This approach emphasizes reachability via an algorithm within the implicit graph structure underlying a recommender dataset and allows us to consider questions relating algorithmic parameters to properties of the datasets. For instance, given a particular algorithm 'jump,' what is the average path length from a person to an artifact? Or, what choices of minimum ratings and jumps maintain a connected graph? We illustrate the approach with a common jump called the 'hammock' using movie recommender datasets.

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Authors and Affiliations

  1. Department of Computer Science, Virginia Tech, Blacksburg, VA, 24061, USA

    Batul J. Mirza & Naren Ramakrishnan

  2. Department of Computer Science, Eastern Michigan University, Ypsilanti, MI, 48197, USA

    Benjamin J. Keller

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  1. Batul J. Mirza
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  2. Benjamin J. Keller
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  3. Naren Ramakrishnan
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Mirza, B.J., Keller, B.J. & Ramakrishnan, N. Studying Recommendation Algorithms by Graph Analysis. Journal of Intelligent Information Systems 20, 131–160 (2003). https://doi.org/10.1023/A:1021819901281

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