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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Griffith, J., O’Riordan, C., & Sorensen, H. (2006). A constrained spreading activation approach to collaborative filtering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4253 LNAI-III, pp. 766–773). Springer Verlag. https://doi.org/10.1007/11893011_97
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