Worcester Polytechnic Institute Electronic Theses and Dissertations Collection

Title page for ETD etd-09244-024515


Document Typethesis
Author NameKim, Yoonsoo
URNetd-09244-024515
Title Addressing the Data Recency Problem in Collaborative Filtering Systems
DegreeMS
DepartmentComputer Science
Advisors
  • David C. Brown, Advisor
  • Mark Claypool, Reader
  • Michael A. Gennert, Department Head
  • Keywords
  • Data recency problem
  • Recommender system
  • Time-based forgetting function
  • Time-based forgetting strategy
  • Collaborative filtering system
  • Date of Presentation/Defense2004-09-23
    Availability unrestricted

    Abstract

    Recommender systems are being widely applied in many E-commerce sites to suggest products, services, and information items to potential users. Collabora-tive filtering systems, the most successful recommender system technology to date, help people make choices based on the opinions of other people. While collaborative filtering systems have been a substantial success, there are sev-eral problems that researchers and commercial applications have identified: the early rater problem, the sparsity problem, and the large scale problem. Moreover, existing collaborative filtering systems do not consider data re-cency. For this reason, if a user¡¯s preferences have changed over time, the sys-tems might not recognize it quickly. This thesis studies how to apply data re-cency to collaborative filtering systems to get more predictive accuracy. We define the data recency problem as the negative impact of old data on the pre-dictive accuracy of collaborative filtering systems. In order to mitigate this shortcoming, the combinations of time-based forgetting mechanisms, pruning and non-pruning strategies and linear and kernel functions, are utilized to ap-ply weights. A clustering technique is employed to detect the user¡¯s changing preferences. We apply our research approach to the DeliBook dataset. The goal of our experiments is to show that our algorithm that incorporates tempo-ral factors provides better recommendations than existing methods.

    Files
  • ykim.pdf

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