Abstract
The increasing acceptance of web recommender systems is mainly due to improvements achieved through intensive research carried out over several years. Numerous methods have been proposed to provide users with more and more reliable recommendations, from the traditional collaborative filtering approaches to sophisticated web mining techniques. In this work, we propose a complete framework to deal with some important drawbacks still present in current recommender systems. Although the framework is addressed to movies’ recommendation, it can be easily extended to other domains. It manages different predictive models for making recommendations depending on specific situations. These models are induced by data mining algorithms using as input data both product and user attributes structured according to a particular domain ontology.
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Moreno, M.N., Segrera, S., López, V.F., Muñoz, M.D., Sánchez, A.L. (2013). Movie Recommendation Framework Using Associative Classification and a Domain Ontology. In: Pan, JS., Polycarpou, M.M., Woźniak, M., de Carvalho, A.C.P.L.F., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2013. Lecture Notes in Computer Science(), vol 8073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40846-5_13
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DOI: https://doi.org/10.1007/978-3-642-40846-5_13
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