Evaluating Recommendation Algorithms by Graph Analysis
<|reference_start|>Evaluating Recommendation Algorithms by Graph Analysis: 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.<|reference_end|>
@article{mirza2001evaluating,
    title={Evaluating Recommendation Algorithms by Graph Analysis},
    author={Batul J. Mirza, Benjamin J. Keller, and Naren Ramakrishnan},
    journal={arXiv preprint arXiv:cs/0104009},
    year={2001},
    archivePrefix={arXiv},
    eprint={cs/0104009},
    primaryClass={cs.IR cs.DM cs.DS}
}