Artículo
Lightweight Tag-Aware Personalized Recommendation on the Social Web Using Ontological Similarity
Xu, Zhenghua; Tifrea-Marciuska, Oana; Lukasiewicz, Thomas; Martinez, Maria Vanina
; Simari, Gerardo
; Chen, Cheng
![Icon](/themes/CONICETDigital/images/conicet.png)
![Icon](/themes/CONICETDigital/images/conicet.png)
Fecha de publicación:
26/06/2018
Editorial:
Institute of Electrical and Electronics Engineers Inc.
Revista:
IEEE Access
ISSN:
2169-3536
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
With the rapid growth of social tagging systems, many research efforts are being put intopersonalized search and recommendation using social tags (i.e., folksonomies). As users can freely choosetheir own vocabulary, social tags can be very ambiguous (for instance, due to the use of homonymsor synonyms). Machine learning techniques (such as clustering and deep neural networks) are usuallyapplied to overcome this tag ambiguity problem. However, the machine-learning-based solutions alwaysneed very powerful computing facilities to train recommendation models from a large amount of data,so they are inappropriate to be used in lightweight recommender systems. In this work, we propose anontological similarity to tackle the tag ambiguity problem without the need of model training by usingcontextual information. The novelty of this ontological similarity is that it first leverages external domainontologies to disambiguate tag information, and then semantically quantifies the relevance between userand item profiles according to the semantic similarity of the matching concepts of tags in the respectiveprofiles. Our experiments show that the proposed ontological similarity is semantically more accurate thanthe state-of-the-art similarity metrics, and can thus be applied to improve the performance of content-based tag-aware personalized recommendation on the Social Web. Consequently, as a model-training-freesolution, ontological similarity is a good disambiguation choice for lightweight recommender systems anda complement to machine-learning-based recommendation solutions.
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Articulos(CCT - BAHIA BLANCA)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - BAHIA BLANCA
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - BAHIA BLANCA
Citación
Xu, Zhenghua; Tifrea-Marciuska, Oana; Lukasiewicz, Thomas; Martinez, Maria Vanina; Simari, Gerardo; et al.; Lightweight Tag-Aware Personalized Recommendation on the Social Web Using Ontological Similarity; Institute of Electrical and Electronics Engineers Inc.; IEEE Access; 6; 26-6-2018; 35590-35610
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