Abstract
We focus on the development of a method to guide the choice of a set of users in an environment where the number of features describing the items is high and user interaction becomes laborious. Using the framework of formal concept analysis, particularly the notion of implication between attributes, we propose a method strongly based on logic which allows to manage the users’ preferences by following a conversational paradigm. Concerning complexity, to build the conversation and provide updated information based on the users’ previous actions (choices) the method has polynomial delay.
Highlights
Current information systems deal with big amounts of data
The results presented in [15] ensure that the elements in MiLHS(Σ) are the smallest sets of attributes providing the minimal generators of the level, on which we can build the step of the conversation
Our approach is strongly based on formal techniques such as formal concept analysis (FCA) and implication logic providing a solid basis and, a good performance
Summary
Current information systems deal with big amounts of data. In these systems two issues have to be addressed: the management of highdimensional data and the interaction with the users. In this paper we focus on the second one. When users search one or several items from sets with very high cardinality, they can become overwhelmed in many cases. Several techniques have been used to approach this problem: within the general area of Information Retrieval, techniques such as Information Filtering [1] help the user to locate items that meet her/his requirements. Recommender systems [2] try to predict which items are more suitable for the user, saving her/him having to select the items
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