Journal of Web Engineering
| Hi-index | 0.00 |
In this research, adaptive user profiles are used to rate Web pages with respect to possible user interest. The user profiles consist of weighted keywords and the adaptation is based on the Hebbian Learning Model with direct user feedback. A user study was conducted to determine if the system would 驴learn驴 over a series of five sessions when there was no explicit task other than to browse. The results are analogous to reading the news, i.e., it is not possible to predict what pages a user will read if there is no explicit task. We suggest that a shift away from content to document style (genre) and other user characteristics may be more effective.