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

Authors:
Stanley Loh;Fabiana Lorenzi;Gabriel Simões;Leandro Krug Wives;José Palazzo M. de Oliveira
Affiliations:
Catholic University of Pelotas and Lutheran University of Brazil, Pelotas, RS, Brasil;Lutheran University of Brazil, Canoas, RS, Brasil;Federal University of Rio, Porto Alegre, RS, Brasil;Federal University of Rio, Porto Alegre, RS, Brasil;Federal University of Rio, Porto Alegre, RS, Brasil
Venue:
Proceedings of the 2008 ACM symposium on Applied computing
Year:
2008

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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.