Evaluating Recommendation Algorithms by Graph Analysis
Information Retrieval
2007-05-23 v1 Discrete Mathematics
Data Structures and Algorithms
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.
Cite
@article{arxiv.cs/0104009,
title = {Evaluating Recommendation Algorithms by Graph Analysis},
author = {Batul J. Mirza and Benjamin J. Keller and Naren Ramakrishnan},
journal= {arXiv preprint arXiv:cs/0104009},
year = {2007}
}