We propose and evaluate the performance of a number of methods for automatic recording of TV programs for digital video servers, which estimate the users' preference over TV programs based on her/his past viewing behavior and automatically record a selected number of TV programs be-lieved to be of intests to the user. Our methods combine the so-called content-based filtering and social (or collaborative) filtering methods and are based ona certain class of on-line learning algorithms known as the 'specialist" algorithms, recently developed in the field of computational learning theory. We empirically evaluated the performance of content-based part of the proposed methds using preference data on TV programs consisting of scores given by people on actual TV programs. The results are largely encouraging and indicate in particular that our methods are practical in terms of both the precision in predicting the user's preference and computational complexity.
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