Studying Recommendation Algorithms by Graph Analysis Export

@article{citeulike:1888810, 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.}, author = {Mirza, Batul J. and Keller, Benjamin J. and Ramakrishnan, Naren}, citeulike-article-id = {1888810}, citeulike-linkout-0 = {http://dx.doi.org/10.1023/A:1021819901281}, day = {1}, doi = {10.1023/A:1021819901281}, journal = {Journal of Intelligent Information Systems}, keywords = {graph, journal, recommender}, month = {March}, number = {2}, pages = {131--160}, posted-at = {2009-11-05 14:42:53}, priority = {5}, title = {Studying Recommendation Algorithms by Graph Analysis}, url = {http://dx.doi.org/10.1023/A:1021819901281}, volume = {20}, year = {2003} }

TY - JOUR ID - citeulike:1888810 L3 - citeulike-article-id:1888810 N2 - 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. IS - 2 JF - Journal of Intelligent Information Systems EP - 160 TI - Studying Recommendation Algorithms by Graph Analysis VL - 20 SP - 131 KW - graph KW - journal KW - recommender AU - Mirza, Batul AU - Keller, Benjamin AU - Ramakrishnan, Naren PY - 2003/03/01/ UR - http://dx.doi.org/10.1023/A:1021819901281 ER -