A comparison of two preference elicitation approaches for museum recommendations

Recommendation systems based on collaborative filtering methods can be exploited in the context of providing personalized artworks tours within a museum. However, to be effectively used, we have several problems to be addressed: user preferences are not expressed as rating and recommendation systems must provide for new users efficient and simple preferences elicitation processes that do not require much effort and time. In this work, we present and evaluate 2 state-of-the-art approaches that share the aim not to rely on individual item ratings. The first method uses a clustering algorithm to categorize items and provide recommendations, while the second one is inspired by the matrix factorization approach to select a couples of item groups that users have to evaluate to obtain preference profiles. We evaluate the 2 approaches with both an off-line simulation and a user study with the aim to find the optimal configuration as well as to evaluate the effectiveness of the 2 proposed methods. Results show that the elicitation processes permit to obtain preference profiles in a time substantially less than the baseline method, while the differences in terms of prediction accuracy are minimal.