Recommender Systems (RS) are information tools designed to suggest items that suit users needs and preferences. They can also support users to browse a product catalogue and better understand and elicit their preferences. These activities are managed by Conversational RSs, which over a series of user-system interactions acquire and revise user preferences by observing the user reaction to proposed options. In this research we focus on the suggestion of queries. We address the problem of helping a user to revise queries for searching in a product catalog with a conversational approach. We want to provide query suggestions that are likely to retrieve products with the largest utility increase, compared to the products retrieved in the previous interaction step. Suggesting query revision is a difficult task given that we do not know the user utility and we do not want to explicitly ask about it. Actually, by observing the query revision selected by the user we can infer some constraints on the user utility function and use this information in order to provide good query revisions. For example, suppose a user queries a product catalogue by issuing a query, such as "I want an hotel with AC and parking". The system, rather than recommending immediately the products that satisfy this query, assumes that the user may have also other needs and suggests some query revisions. A new query may add an additional feature to the current query, e.g., "are you interested also in sauna?". Products with more features, if available, will surely increase the user utility. But not all features are equally important for the user.
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