Evaluating Recommendation Algorithms by Graph Analysis

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dc.creatorMirza, Batul J.
dc.creatorKeller, Benjamin J.
dc.creatorRamakrishnan, Naren
dc.date2001-04-03
dc.date.accessioned2026-07-07T03:17:04Z
dc.date.available2026-07-07T03:17:04Z
dc.descriptionWe 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.
dc.identifierhttps://arxiv.org/abs/cs/0104009
dc.identifierhttp://arxiv.org/abs/cs/0104009
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30585
dc.subjectInformation Retrieval
dc.subjectDiscrete Mathematics
dc.subjectData Structures and Algorithms
dc.subjectH.4.2
dc.titleEvaluating Recommendation Algorithms by Graph Analysis
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