Enhanced Prediction Algorithm for Item-Based Collaborative Filtering Recommendation

  • Heung-Nam Kim
  • Ae-Ttie Ji
  • Geun-Sik Jo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4082)


As the Internet infrastructure has been developed, a substantial number of diverse effective applications have attempted to achieve the full potential offered by the infrastructure. Collaborative Filtering recommender system, one of the most representative systems for personalized recommendations in E-commerce on the Web, is a system assisting users in easily finding the useful information. But traditional collaborative filtering suffers some weaknesses with quality evaluation: the sparsity of the data, scalability, unreliable users. To address these issues, we have presented a novel approach to provide the enhanced prediction quality supporting the protection against the influence of malicious ratings, or unreliable users. In addition, an item-based approach is employed to overcome the sparsity and scalability problems. The proposed method combines the item confidence and item similarity, collectively called item trust using this value for online predictions. The experimental evaluation on MovieLens datasets shows that the proposed method brings significant advantages both in terms of improving the prediction quality and in dealing with malicious datasets.


Neighborhood Size Collaborative Filter Prediction Quality Collaborative Filter Algorithm Item Trust 
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

  • Heung-Nam Kim
    • 1
  • Ae-Ttie Ji
    • 1
  • Geun-Sik Jo
    • 2
  1. 1.Intelligent E-Commerce Systems Laboratory, Department of Computer Science & Information EngineeringInha University 
  2. 2.School of Computer Science & EngineeringInha UniversityIncheonKorea

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