Batul J Mirza, Benjamin J. Keller, Naren Ramakrishnan

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.

Supporting Documents

People

Ramakrishnan-updated

Naren Ramakrishnan


Publication Details

Date of publication:
March 3, 2003
Journal:
Journal of Intelligent Information Systems
Page number(s):
131--160
Volume:
20
Issue Number:
2