Hedonic Recommendations: An Econometric Application on Big Data

This work will demonstrate how economic theory can be applied to big data analysis. To do this, I propose two layers of machine learning that use econometric models introduced into a recommender system. The reason for doing so is to challenge traditional recommendation approaches. These approaches are inherently biased due to the fact that they ignore the final preference order for each individual and under-specify the interaction between the socio-economic characteristics of the participants and the characteristics of the commodities in question. In this respect, our hedonic recommendation approach proposes to first correct the internal preferences with respect to the tastes of each individual under the characteristics of given products. In the second layer, the relative preferences across participants are predicted by socio-economic characteristics. The robustness of the model is tested with the MovieLens (100k data consists of 943 users over 1682 movies) run by GroupLens. Our methodology shows the importance and the necessity of correcting the data set by using economic theory. This methodology can be applied for all recommender systems using ratings based on consumer decisions.