Abstract
In this paper, we describe a collaborative filtering approach that aims to use features of users and items to better represent the problem space and to provide better recommendations to users. The goal of the work is to show that a graph-based representation of the problem domain, and a constrained spreading activation approach to effect retrieval, has as good, or better, performance than a traditional collaborative filtering approach using Pearson Correlation. However, in addition, the representation and approach proposed can be easily extended to incorporate additional information.
| Original language | English (Ireland) |
|---|---|
| Title of host publication | 10th International Conference on Knowledge-Based Intelligent Information Engineering Systems (KES), Invited Session on Recommender Agents and Adaptive Web-based Systems |
| Publication status | Published - 1 Oct 2006 |
Authors (Note for portal: view the doc link for the full list of authors)
- Authors
- Josephine Griffith, Colm O'Riordan, Humphrey Sorensen