E-commerce companies have developed many methods and tools in order to personalize their web sites and services according to users’ necessities and tastes. The most successful and widespread are the recommender systems. The aim of these systems is to lead people to interesting items through recommendations. Sometimes, these systems face situations in which there is a lack of information and this implies unsuccessful results. In this chapter we propose a knowledge based recommender system designed to overcome these situations. The proposed system is able to compute recommendations from scarce information. Our proposal will consist in gathering user’s preference information over several examples using an incomplete preference relation. The system will complete this relation and exploit it in order to obtain a user profile that will be utilized to generate good recommendations.
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Martínez, L., Pérez, L.G., Barranco, M.J., Espinilla, M. (2008). A Knowledge Based Recommender System Based on Consistent Preference Relations. In: Da Ruan, Hardeman, F., van der Meer, K. (eds) Intelligent Decision and Policy Making Support Systems. Studies in Computational Intelligence, vol 117. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78308-4_6
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