Content-based recommender systems suggest documents, items, and services to users based on learning a profile of the user from rated examples containing information about the given items. Text categorization methods are very useful for this task but generally rely on unstructured text. We have developed a book-recommending system that utilizes semi-structured information about items gathered from the web using simple information extraction techniques. Initial experimental results demonstrate that this approach can produce fairly accurate recommendations.

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In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98)"-REC-WKSHP98, year="1998, 70-74, Madison, WI, 1998.

@InProceedings{mooney:rec-wkshp98, title={Book Recommending Using Text Categorization with Extracted Information}, author={Raymond J. Mooney and Paul N. Bennett and Loriene Roy}, booktitle={Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98)"-REC-WKSHP98, year="1998}, address={Madison, WI}, pages={70-74}, url="http://www.cs.utexas.edu/users/ai-lab/pub-view.php?PubID=51496", year={1998} }