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A Hybrid Recommendation Approach for One-and-Only Items

  • Xuetao Guo
  • Guangquan Zhang
  • Eng Chew
  • Steve Burdon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3809)

Abstract

Many mechanisms have been developed to deliver only relevant information to the web users and prevent information overload. The most popular recent developments in the e-commerce domain are the user-preference based personalization and recommendation techniques. However, the existing techniques have a major drawback – poor accuracy of recommendation on one-and-only items – because most of them do not understand the item’s semantic features and attributes. Thus, in this study, we propose a novel Semantic Product Relevance model and its attendant personalized recommendation approach to assist Export business selecting the right international trade exhibitions for market promotion. A recommender system, called Smart Trade Exhibition Finder (STEF), is developed to tailor the relevant trade exhibition information to each particular business user. STEF reduces significantly the time, cost and risk faced by exporters in selecting, entering and developing international markets. In particular, the proposed model can be used to overcome the drawback of existing recommendation techniques.

Keywords

Recommender System Semantic Similarity Collaborative Filter Mean Absolute Error Recommendation Technique 
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 2005

Authors and Affiliations

  • Xuetao Guo
    • 1
  • Guangquan Zhang
    • 1
  • Eng Chew
    • 1
  • Steve Burdon
    • 2
  1. 1.Faculty of Information TechnologyUniversity of Technology SydneyBroadwayAustralia
  2. 2.Faculty of BusinessUniversity of Technology SydneyBroadwayAustralia

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