Exploiting Similarity Measures in Multi-criteria Based Recommendations

Abstract

The need for developing efficient and effective recommender systems has lately become fundamental, basically due to the vast amount of on-line information and the increasing popularity of Internet applications. Such systems are based on various recommendation techniques, which aim at guiding users to survey objects that appear as interesting or useful to them. By exploiting the concept of fuzzy similarity measures, this paper presents a recommendation framework that builds on the strengths of knowledge-based and collaborative filtering techniques. Following a multi-criteria approach, the proposed framework is able to provide users with a ranked list of alternatives, while it also permits them to submit their evaluations on the existing objects of the database. Much attention is given to the extent in which the user evaluation may affect the values of the stored objects. The applicability of our approach is demonstrated through a web-based tool that provides recommendations about visiting different cities of a country.