Abstract
Recent Recommender Systems (RSs) research has focused on identifying and understanding factors that determine the choice behaviour of their users. By simulating users’ choices, influenced by RSs, it was shown that algorithmic biases, such as the tendency to recommend popular items, are transferred to the users’ choices. In this position paper, we briefly summarise previous results showing that the effect of an RS on the quality and distribution of the users’ choices can be influenced by the users’ tendency to prefer certain types of items, i.e., popular, recent, or highly-rated items. To quantify this impact, we have defined alternative Choice Models (CMs) and simulated their effect when users are exposed to recommendations. We found that a bias determined by an RS, e.g., the tendency to concentrate the choices on a restricted number of items, can also be enforced by the CM. Moreover, we have discovered that the quality of the choices can be jeopardised by a CM. We also found that for some RSs, the impact of the CM is less prominent, and their biases are not modified by the CM. This research line shows the importance of assessing algorithmic biases in conjunction with a proper model of users’ behaviour.