A Hybrid Similarity Concept for Browsing Semi-structured Product Items

  • Markus Zanker
  • Sergiu Gordea
  • Markus Jessenitschnig
  • Michael Schnabl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4082)


Personalization, information filtering and recommendation are key techniques helping online-customers to orientate themselves in e-commerce environments. Similarity is an important underlying concept for the above techniques. Depending on the representation mechanism of information items different similarity approaches have been established in the fields of information retrieval and case-based reasoning. However, many times product descriptions consist of both, structured attribute value pairs and free-text descriptions. Therefore, we present a hybrid similarity approach from information retrieval and case-based recommendation systems and enrich it with additional knowledge-based concepts like threshold values and explanations. Furthermore, we implemented our hybrid similarity concept in a service component and give evaluation results for the e-tourism domain.


Recommender System Domain Expert Negative Preference Hybrid Similarity Reference Item 
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

  • Markus Zanker
    • 1
  • Sergiu Gordea
    • 1
  • Markus Jessenitschnig
    • 1
  • Michael Schnabl
    • 1
  1. 1.University KlagenfurtKlagenfurtAustria

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