Abstract
Most recommendation systems use only one recommendation criterion at the time, often the accuracy. However, this approach is limited and can generate biased recommendations. In this work, a content-based recommendation system for movie recommendation supported by a Multi-objective Evolutionary Algorithm is proposed. The proposed system takes advantage of Multi-objective optimization and uses three criteria: accuracy, diversity, and novelty of the recommendation. The system uses a multi-objective algorithm to solve the recommendation problem. It will be evaluated using Movielens and IMDB movie datasets and its results will be compared with the state of the art recommendation systems.
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Almeida, M.S., Britto, A. (2020). MOEA-RS: A Content-Based Recommendation System Supported by a Multi-objective Evolutionary Algorithm. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2020. Lecture Notes in Computer Science(), vol 12416. Springer, Cham. https://doi.org/10.1007/978-3-030-61534-5_24
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