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Evaluating Recommendation Algorithms by Graph Analysis

Batul J. Mirza·Benjamin J. Keller·Naren Ramakrishnan
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Abstract

We present a novel framework for evaluating 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 serves as a complement to evaluation in terms of predictive accuracy. The framework 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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Batul J. Mirza, Benjamin J. Keller, Naren Ramakrishnan. 2001-04-03. Evaluating Recommendation Algorithms by Graph Analysis. https://arxiv.org/abs/cs/0104009

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