## Studying Recommendation Algorithms by Graph Analysis (2003)

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Citations: | 24 - 0 self |

### BibTeX

@MISC{Mirza03studyingrecommendation,

author = {Batul J. Mirza and Naren Ramakrishnan and Benjamin J. Keller},

title = {Studying Recommendation Algorithms by Graph Analysis},

year = {2003}

}

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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.