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papers > bezerra02
Byron Bezerra and Francisco Carvalho and Geber Ramalho and Jean-Daniel Zucker (2002).
Speeding up Recommendation Systems.
Proceedings of the AHÂ’2002 Workshop on Personalization in Future TV pp..
http://www.di.unito.it/~liliana/TV02/completeProceedings.pdf
Abstract
Recommender Systems aims to furnish automatic recommendations based on information recorded about user preferences and uses Information Filtering techniques to manage this information and provide the user with options, which will present greater possibility to satisfy the user. Content-based filtering is one of the most useful approaches used for Information Filtering. Often in this approach, the recommendation is based on a comparison between a user profile and the items. Therefore, the system selects those items that score the best according to a particular criterion. This paper presents an approach where each user profile is modeled by a meta-prototype and the comparison between an item and a profile is based on a suitable matching function which possess two components: position and content.
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