Understanding Transparent and Complicated Users as Instances of Preference Learning for Recommender Systems

In this paper we are concerned with user understanding in content based recommendation. We assume having explicit ratings with time-stamps from each user. We integrate three different movie data sets, trying to avoid features specific for single data and try to be more generic. We use several metrics which were not used so far in the recommender systems domain. Besides classical rating approximation with RMSE and ratio of order agreement we study new metrics for predicting Next-k and (at least) 1-hit at Next-k. Using these Next-k and 1-hit we try to model display of our recommendation - we can display k objects and hope to achieve at least one hit. We trace performance of our methods and metrics also as a distribution along each single user. We define transparent and complicated users with respect to number of methods which achieved at least one hit. We provide results of experiments with several combinations of methods, data sets and metrics along these three axes.