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A Constrained Spreading Activation Approach to Collaborative Filtering

  • Josephine Griffith
  • Colm O’Riordan
  • Humphrey Sorensen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4253)

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.

Keywords

Recommender System Weighted Edge Threshold Function Collaborative Filter Link Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Josephine Griffith
    • 1
  • Colm O’Riordan
    • 1
  • Humphrey Sorensen
    • 2
  1. 1.Dept. of Information TechnologyNational University of IrelandGalwayIreland
  2. 2.Dept. of Computer ScienceUniversity College CorkCorkIreland

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