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

Published: 16 March 2008 Publication History

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.

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  • (2020)WordRecommenderProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3428658.3431093(181-184)Online publication date: 30-Nov-2020

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

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    cover image ACM Conferences
    SAC '08: Proceedings of the 2008 ACM symposium on Applied computing
    March 2008
    2586 pages
    ISBN:9781595937537
    DOI:10.1145/1363686
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    Published: 16 March 2008

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    Author Tags

    1. classification
    2. ontologies
    3. recommender systems
    4. text analysis

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    March 16 - 20, 2008
    Fortaleza, Ceara, Brazil

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    • (2020)WordRecommenderProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3428658.3431093(181-184)Online publication date: 30-Nov-2020

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