Abstract
Recommender systems are needed to find food items of one’s interest. This paper reviews recommender systems and recommendation methods, then propose a food personalization framework based on adaptive hypermedia and extend Hermes framework with food recommendation functionality. Moreover, it combines TF-IDF term extraction method with cosine similarity measure. Healthy heuristics and standard food database are incorporated into the knowledgebase. Based on the performed evaluation, we conclude that semantic recommender systems in general outperform traditional recommenders systems with respect to accuracy, precision, and recall, and that the proposed recommender has a better F-measure than existing semantic recommenders.
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El-Dosuky, M.A., Rashad, M.Z., Hamza, T.T., EL-Bassiouny, A.H. (2012). Food Recommendation Using Ontology and Heuristics. In: Hassanien, A.E., Salem, AB.M., Ramadan, R., Kim, Th. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2012. Communications in Computer and Information Science, vol 322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35326-0_42
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DOI: https://doi.org/10.1007/978-3-642-35326-0_42
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