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    Quantifying the multi-user account problem for collaborative filtering based recommender systems

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    EDWARDS-MASTERS-REPORT.pdf (543.8Kb)
    Date
    2009-12
    Author
    Edwards, James Adrian
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    Abstract
    Identification based recommender systems make no distinction between users and accounts; all the data collected during account sessions are attributed to a single user. In reality this is not necessarily true for all accounts; several different users who have distinct, and possibly very different, preferences may access the same account. Such accounts are identified as multi-user accounts. Strangely, no serious study considering the existence of multi-user accounts in recommender systems has been undertaken. This report quantifies the affect multi-user accounts have on the predictive capabilities of recommender system, focusing on two popular collaborative filtering algorithms, the kNN user-based and item-based models. The results indicate that while the item-based model is largely resistant to multi-user account corruption the quality of predictions generated by the user-based model is significantly degraded.
    Description
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    Subject
    Recommender systems
    Collaborative filtering
    Multi-user accounts
    URI
    http://hdl.handle.net/2152/ETD-UT-2009-12-460
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    © The University of Texas at Austin