A new perspective on recommender systems: A class path information model

Recommender systems perform suggestions for items that might interest the users. The recommendation process is usually performed at the level of a single item, i.e., for each item not evaluated by a user, classic approaches look for the rating given by similar users for that item, or for an item with similar content. This leads to the so-called overspecialization/serendipity problem, in which the recommended items are trivial and users do not come across surprising items. In this paper we first show that the preferences of the users are actually distributed over a small set of classes of items, leading the recommended items to be too similar to the ones already evaluated. We also present a novel representation model, named Class Path Information (CPI), able to express the current and future preferences of the users in terms of a ranked set of classes of items. Our approach to user preferences modeling is based on a semantic analysis of the items evaluated by the users, in order to extend the ground truth and predict where the future preferences of the users will go. Experimental results show that our approach, by including in the CPI model the same classes predicted by a state-of-the-art recommender system, is able to accurately model the preferences of the users in terms of classes and not in terms of single items, allowing recommender systems to suggest non trivial items.