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    Comparing keywords and taxonomies in the representation of users profiles in a content-based recommender system

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    This work investigates the use of keywords and classes to represent user's profiles in order to improve a content-based recommender system. The techniques were implemented and tested in a recommender system for a website that gathers commercial ads. Ads are posted by individuals and contain a title and a textual description. Profiles are created and maintained through the analysis of ads seen by the user during a certain period of time and may be represented by classes, keywords or both kinds. Keywords are automatically extracted from the textual description of the ads. Classes come from a taxonomy defined by the website. Ads must be posted within a leaf class of the taxonomy. The items to be recommended are ads containing keywords associated to the user in his/her profile and/or ads classified in the leaf-classes present in the user's profile. The paper demonstrates that the combination of both techniques (keywords and classes) outperforms the use of each one separately.

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    Description

    Title : Comparing keywords and taxonomies in the representation of users profiles in a content-based recommender system
    Author(s) : Stanley Loh, Fabiana Lorenzi, Gabriel Simões, Leandro Krug Wives, José Palazzo M De Oliveira
    Abstract : This work investigates the use of keywords and classes to represent user's profiles in order to improve a content-based recommender system. The techniques were implemented and tested in a recommender system for a website that gathers commercial ads. Ads are posted by individuals and contain a title and a textual description. Profiles are created and maintained through the analysis of ads seen by the user during a certain period of time and may be represented by classes, keywords or both kinds. Keywords are automatically extracted from the textual description of the ads. Classes come from a taxonomy defined by the website. Ads must be posted within a leaf class of the taxonomy. The items to be recommended are ads containing keywords associated to the user in his/her profile and/or ads classified in the leaf-classes present in the user's profile. The paper demonstrates that the combination of both techniques (keywords and classes) outperforms the use of each one separately.
    Subject : unspecified
    Area : Other
    Language : English
    Year : 2008

    Affiliations UFRGS
    Journal : Proceedings of the 2008 ACM symposium on Applied computing SAC 0
    Publisher : ACM Press
    Pages : 2030
    Url : http://portal.acm.org/citation.cfm?doid=1363686.1364177
    Doi : 10.1145/1363686.1364177

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