Incorporating user control into recommender systems based on naive bayesian classification

Recommender systems are increasingly being employed to personalize services, such as on the web, but also in electronics devices, such as personal video recorders. These recommenders learn a user profile, based on rating feedback from the user on, e.g., books, songs, or TV programs, and use machine learning techniques to infer the ratings of new items. The techniques commonly used are collaborative filtering and naive Bayesian classification, and they are known to have several problems, in particular the cold-start problem and its slow adaptivity to changing user preferences. These problems can be mitigated by allowing the user to set up or manipulate his profile. In this paper, we propose an extension to the naive Bayesian classifier that enhances user control. We do this by maintaining and flexibly integrating two profiles for a user, one learned by rating feedback, and one created by the user. We in particular show how the cold-start problem is mitigated.