Ghosh, Joydeep2010-09-152010-09-152017-05-112010-09-152010-09-152017-05-112009-12December 2http://hdl.handle.net/2152/ETD-UT-2009-12-460textIdentification 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.application/pdfengRecommender systemsCollaborative filteringMulti-user accountsthesis2010-09-15