期刊名称：Documents de Travail du Centre d'Economie de la Sorbonne
印刷版ISSN：1955-611X
出版年度：2017
出版社：Centre d'Economie de la Sorbonne
摘要：This work will demonstrate how economic theory can be applied to big data analysis. Todo this, I propose two layers of machine learning that use econometric models introducedinto a recommender system. The reason for doing so is to challenge traditionalrecommendation approaches. These approaches are inherently biased due to the fact thatthey ignore the final preference order for each individual and under-specify theinteraction between the socio-economic characteristics of the participants and thecharacteristics of the commodities in question. In this respect, our hedonicrecommendation approach proposes to first correct the internal preferences with respectto the tastes of each individual under the characteristics of given products. In the secondlayer, the relative preferences across participants are predicted by socio-economiccharacteristics. The robustness of the model is tested with the MovieLens (100k dataconsists of 943 users over 1682 movies) run by GroupLens. Our methodology shows theimportance and the necessity of correcting the data set by using economic theory. Thismethodology can be applied for all recommender systems using ratings based onconsumer decisions.
