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Serendipitous Fuzzy Item Recommendation with ProfileMatcher

  • Danilo Dell’Agnello
  • Anna Maria Fanelli
  • Corrado Mencar
  • Massimo Minervini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6857)

Abstract

In this paper an approach to serendipitous item recommendation is outlined. The model used for this task is an extension of ProfileMatcher, which is based on fuzzy metadata describing both user and items to be recommended. To address the task of recommending serendipitous resources, a priori knowledge on the relations occurring among metadata values is injected in the recommendation process. This is achieved using fuzzy graphs to model similarity relations among the elements of the fuzzy sets describing the metadata. An experimentation has been carried out on the MovieLens data set to show the impact of serendipity injection in the item recommendation process.

Keywords

Jaccard Index Fuzzy Graph Recommendation Process Item Recommendation Mender System 
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 2011

Authors and Affiliations

  • Danilo Dell’Agnello
    • 1
  • Anna Maria Fanelli
    • 1
  • Corrado Mencar
    • 1
  • Massimo Minervini
    • 1
  1. 1.Dept. of InformaticsUniversity of BariBariItaly

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