The task of guiding in adaptive recommender systems
In this paper, we study the recommendation problem as formed by two tasks: (i) to filter useful/interesting items, (ii) to guide the user to good recommendations. The first task has been widely studied in the field of recommender systems. In fact, the most common characterization of these systems is based on the algorithms that select (filter) the items to be recommended (e.g. collaborative filtering, content-based, etc.). Through this paper, we will focus on the second task: the task of guiding the user. We claim that this task needs a closer attention. In this paper, we report an experiment to provide evidence for this fact. Actually, the experiment shows that machine learning algorithms commonly applied to the first task become useless when applied to the task of guiding.
recommender systems adaptive web based systems interactive web based systems user profiles user models