Recommender Systems (RS) are software applications which aim to support users in their decision making while interacting with large information spaces. Most recommender systems are designed for recommending items to individuals. In this paper we provide experimental results related to developing a content-based group recommender system. To this end we make two important contributions. (1) Implementation of a group recommender system based on decision-lists as proposed recently in (Padmanabhan et al., 2011) using MovieLens dataset which is a relatively huge data-set (100,000 ratings from 943 users on 1682 movies) as compared to the data-set size of 150 used in (Padmanabhan et al., 2011) (2) We use seven variants of decision-tree measures and built an empirical comparison table to check for precision rate in group recommendation based on different social-choice theory strategies.