The huge amount of information available in the currently evolving world wide information infrastructure at any one time can easily overwhelm end-users. One way to address the information explosion is to use an "information filtering agent" which can select information according to the interest and/or need of an end-user. However, at present few such information filtering agents exist. In this study, we evaluate the use of feature-based approaches to user modcling with the purpose of creating a filtering agent for the video-on-demand application. We evaluate several feature and clique-based models for 10 voluntary subjects who provided ratings for the movies. Our preliminary results suggest that feature-based selection can be a useful tool to recommend movies according to the taste of the user and can be as effective as a movie rating expert. We compare our feature-based approach with a clique-based approach, which has advantages whcrc information from other users is available.
We have measured the cross sections at 90° cm. for n* and 7r° photoproduction with polarized photons. The photon energies ranged from 0.8 to 2.2 GeV. We compare the resonant "bumps" in the cross section with theoretical models. The measured asymmetry agrees with a quark-model calculation though the predicted cross sections are low.
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