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      A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem

      Ahn, Hyung Jun
      DOI
       10.1016/j.ins.2007.07.024
      Link
       www.sciencedirect.com
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      Citation
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      Ahn, H. J. (2008). A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Information Sciences, 178(1), 37-51.
      Permanent Research Commons link: https://hdl.handle.net/10289/1976
      Abstract
      Collaborative filtering is one of the most successful and widely used methods of automated product recommendation in online stores. The most critical component of the method is the mechanism of finding similarities among users using product ratings data so that products can be recommended based on the similarities. The calculation of similarities has relied on traditional distance and vector similarity measures such as Pearson’s correlation and cosine which, however, have been seldom questioned in terms of their effectiveness in the recommendation problem domain. This paper presents a new heuristic similarity measure that focuses on improving recommendation performance under cold-start conditions where only a small number of ratings are available for similarity calculation for each user. Experiments using three different datasets show the superiority of the measure in new user cold-start conditions.
      Date
      2008
      Type
      Journal Article
      Publisher
      Elsevier
      Collections
      • Management Papers [1136]
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