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Dynamic learning and refinement of preferences through keywords
Perelló Cruz, David
Moreno Ribas, Antonio; Marin Isern, Lucas
The main goal in this work is to learn user preferences in situations where the objects to be treated are formed only by textual information and we continuously have information of selections made by the user. This work has been divided in two major parts: the first one including the algorithms and techniques to rank a set of alternatives, and the second one including the techniques to maintain the profile up to date. Regarding the first part, the goal is to evaluate an object of type text, i.e. given the user preferences to assign the degree of potential interest on that object. This will allow us to evaluate the set of alternatives and to sort them according to the user preferences. Concerning the second part, the main goal is to design a method to update the user profile, given the user selection from a set of alternatives in the first part. This method will allow to adapt a user profile in an unsupervised and dynamic way. To achieve these objectives it is necessary to fulfil the tasks discussed in this document and named below in the document organization.
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Sistemes experts
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Agents intel·ligents
Sistemes experts (Informàtica)
Recommender systems (Information filtering)
Expert systems (Computer science)
Sistemes recomanadors (Filtratge d'informació)
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/masterThesis
Universitat Politècnica de Catalunya
         

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