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Towards Information Enrichment through Recommendation Sharing

  • Li-Tung Weng
  • Yue Xu
  • Yuefeng Li
  • Richi Nayak

Abstract

Nowadays most existing recommender systems operate in a single organisational basis, i.e. a recommender system recommends items to customers of one organisation based on the organisation’s datasets only. Very often the datasets of a single organisation do not have sufficient resources to be used to generate quality recommendations. Therefore, it would be beneficial if recommender systems of different organisations with similar nature can cooperate together to share their resources and recommendations. In this chapter, we present an Ecommerce-oriented Distributed Recommender System (EDRS) that consists of multiple recommender systems from different organisations. By sharing resources and recommendations with each other, these recommenders in the distributed recommendation system can provide better recommendation service to their users. As for most of the distributed systems, peer selection is often an important aspect. This chapter also presents a recommender selection technique for the proposed EDRS, and it selects and profiles recommenders based on their stability, average performance and selection frequency. Based on our experiments, it is shown that recommenders’ recommendation quality can be effectively improved by adopting the proposed EDRS and the associated peer selection technique.

Keywords

Recommender System Architectural Style Selection Frequency User Cluster Recommendation Quality 
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 Science+Business Media, LLC 2009

Authors and Affiliations

  • Li-Tung Weng
    • 1
  • Yue Xu
    • 1
  • Yuefeng Li
    • 1
  • Richi Nayak
    • 1
  1. 1.Queensland University of TechnologyBrisbaneAustralia

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