Combination of user profile information and collaborative filtering in recommendations

This paper analyses how information from user profile influences quality of recommendations. We first start with an overview of recommendation systems, their functions methods used. The empirical part focuses on collaborative filtering method with the aim to find improvement of recommendations based on the user profile. The main objective for realized experiments was to verify the hypothesis that using information stored in user's profiles can improve collaborative filtering recommendation results. All our experiments were realized in RapidMiner tool on well-known MovieLens dataset. For evaluation of results we used standard metrics such as RMSE, MAE and NMAE. Experiments did not confirm the above mentioned hypothesis, but we present additional analysis of identified clusters with best and worst recommendation results.