Automatic preference learning on semantic attributes

Entity: Universitat Rovira i Virgili (URV)

Confidenciality: No

Education area(s): Enginyeria Informàtica, Seguretat Informàtica i Sistemes Intel·ligents

Title in different languages: Automatic preference learning on semantic attributes

Abstract: Summary Recommender systems need to know the preferences of users to provide accurate suggestions. This information may be learnt by analysing the interaction of the user with the system. Previous works showed how to learn the preferences on numeric and categorical attributes. In this work we propose a framework to learn the preferences on uni-valued and multi-valued semantic attributes. The possibility to tune the learning parameters to obtain a good performance is shown in a case study of tourist destinations.

Subject: Enginyeria

Academic year: 2015-2016

Language: Anglès

Work's public defense date: 2016-09-02

Subject areas: Computer engineering

Student: Naha, Kallol

Department: Enginyeria Informàtica i Matemàtiques

TFM credits: 12

Creation date in repository: 2016-11-15

Keywords: Recommendation, Ontology, Semantic Attributes

Title in original language: Automatic preference learning on semantic attributes

Project director: Moreno Ribas, Antonio