Henry Blanco

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Information Recommendation is a conversational approach aimed at suggesting to the user how to reformulate his queries to a product catalogue in order to find the products that maximize his utility. In previous work, it was shown that, by observing the queries selected by the user among those suggested, the system can make inferences on the true user(More)
Observing the queries selected by a user, among those suggested by a recommender system, one can infer constraints on the user’s utility function, and can avoid suggesting queries that retrieve products with an inferior utility, i.e., dominated queries. In this paper we propose a new efficient technique for the computation of dominated queries. It relies on(More)
Query revisions in a conversational system can be efficiently computed by assuming that the profiles of the potential users are in a predefined, a priori known and finite set. However, without any additional knowledge of the actual profiles distribution, the system may miss the true profiles of the users, hence deteriorating the system performance. We(More)
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