A recommender system is a tool employed to filter the huge amounts of data that companies have to deal with, and produce effective suggestions to the users. The estimation of the interest of a user toward an item, however, is usually performed at the level of a single item, i.e., for each item not evaluated by a user, canonical approaches look for the rating given by similar users for that item, or for an item with similar content. Such approach leads toward the so-called overspecial-ization/serendipity problem, in which the recommended items are trivial and users do not come across surprising items. This work first shows that user preferences are actually distributed over a small set of classes of items, leading the recommended items to be too similar to the ones already evaluated, then we propose a novel model, named Class Path Information (CPI), able to represent the current and future preferences of the users in terms of a ranked set of classes of items. The proposed approach is based on a semantic analysis of the items evaluated by the users, in order to extend their ground truth and infer the future preferences. The performed experiments 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 user preferences in terms of classes, instead of in terms of single items, allowing to recommend non trivial items.
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