Mejora de las recomendaciones en algoritmos conversacionales basados en experiencias

Abstract

In recent years there has been an exponential growth of the new technologies. The great expansion of internet and easy access to the network has increased the number of internet data and behold the context of the problem: the difficulty of finding the right information.Nowadays million of users access for internet searching products and information, cause of this it’s necessary the use of recommenders which allow users to find out what they are looking for. At the same time, most interactive applications of recommenders are a key business factor for those who sell their products through the network, enabling them to increase their incomes if they use a recommender system suitable. This project is focused on recommender systems based on critics (critiquing), which allow the user to indicate by a feedback, the characteristics of the product you are looking for in order to provide products according to their needs and preferences. Continuing the methodology Critiquing, a study of standard algorithms (STD) and incremental (IC) will be performed, as well as those belonging to the category Experience-based Critiquing: EBC, HAC, HGR, HOR and Graph based, all based on Unit Critiquing. In addition, the algorithms previously mentioned will be implemented to be used by users through Compound Critiques. These allow the user to provide simultaneously a feedback about multiples characteristics of the product. This fact differentiates them from Unit Critiques, which only provide information on a single characteristic. In this project an analysis algorithms will be held Unit Critiques vs. Compound Critiques. The result of this analysis will allow to reflect optimization that occurs in a recommender process if Compound Critiques are used.