Automatic generation of content-based user profiles compared to rule-based profiles for information filtering

In this paper content-based filtering approach is compared with rule-based filtering approach. The research goals are to study the effectiveness of automatic generation of content based user profiles as a function of the number of examples given to the system, and to compare it with the effectiveness of a rule-based user profile. The study shows that rule based profiles tend to outperform content-based profiles when the latter are generated from a small number of positive examples, but as the number of examples increases, content-based profiles are more advantageous. We conclude that both approaches can be combined, thus minimizing the users’ initial effort of building filtering profiles and
enabling incremental improvements based on user feedback later on.