User Profiling vs. Accuracy in Recommender System User Experience

A Recommender System (RS) filters a large amount of information to identify the items that are likely to be more interesting and attractive to a user. Recommendations are inferred on the basis of different user profile characteristics, in most cases including explicit ratings on a sample of suggested elements. RS research highlights that profile length, i.e., the number of collected ratings, is positively correlated to the accuracy of recommendations, which is considered an important quality factor for RSs. Still, gathering ratings adds a burden on the user, which may negatively affect the UX. A design tension seems to exist, induced by two conflicting requirements - to raise accuracy by increasing the profile length, and to make the profiling process smooth for the user by limiting the number of ratings. The paper presents a wide empirical study (1080 users involved) which explores this issue. Our work attempts to identify which of the two contrasting forces influenced by profile length - recommendations accuracy and burden of the rating process - has stronger effects on the perceived quality of the UX with a RS.