Document Type

Article

Publication Date

2005

Publication Source

Proceedings of the International ACM Intelligent User Interfaces Beyond Personalization Recommender Systems Research Workshop

Abstract

Our work is based on the premise that analysis of the connections exploited by a recommender algorithm can provide insight into the algorithm that could be useful to predict its performance in a fielded system. We use the jumping connections model defined by Mirza et al. [6], which describes the recommendation process in terms of graphs. Here we discuss our work that has come out of trying to understand algorithm behavior in terms of these graphs. We start by describing a natural extension of the jumping connections model of Mirza et al., and then discuss observations that have come from our studies, and the directions in which we are going.

Document Version

Postprint

Comments

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in the conference proceedings.

Permission documentation is on file.

Publisher

Association for Computing Machinery

Peer Reviewed

yes