& This article presents a new technology called interactive query management (IQM), designed for supporting flexible query management in decision support systems and recommender systems. IQM aims at guiding a user to refine a query to a structured repository of items when it fails to return a manageable set of products. Two failure conditions are considered here, when a query returns either too many products or no product at all. In the former case, IQM uses feature selection methods to suggest some features that, if used to further constrain the current query, would greatly reduce the result set size. In the latter case, the culprits of the failure are determined by a relaxation algorithm and explained to the user, enumerating the constraints that, if relaxed, would solve the ''no results'' problem. As a consequence, the user can understand the causes of the failure and decide what is the best query relaxation. After having presented IQM, we illustrate its empirical evaluation. We have conducted two types of experiments, with real users and offline simulations. Both validation procedures show that IQM can repair a large percentage of user queries and keep alive the human computer interaction until the user information goals are satisfied.
This chapter provides the methodological foundations and the rationale for approaching travel destination recommendation as a problem-solving activity. Case-base reasoning (CBR) is presented both as a cognitive plausible approach and as an integrated paradigm to build advisory systems. The chapter then describes an integrated solution called Trip@dvice that employs CBR. Finally, the chapter discusses the most important findings of various validation activities and future research.
Abstract. This paper presents a new technology for supporting flexible query management in recommender systems. It is aimed at guiding a user in refining her query when it fails to return any item. It allows the user to understand the culprit of the failure and to decide what is the best compromise to chose. The method uses the notion of hierarchical abstraction among a set of features, and tries to relax first the constraint on the feature with lowest abstraction, hence with the lightest revision of the original user needs. We have introduced this methodology in a travel recommender system as a query refinement tool used to pass the returned items by the query to a case-based ranking algorithm, before showing the query results to the user. We discuss the results of the empirical evaluation which shows that the method, even if incomplete, is powerful enough to assist the users most of the time.
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