A Fuzzy-Based Approach for Modelling Preferences of Users in Multi-Criteria Recommender Systems

Abstract

Recommender systems (RSs) are web-based tools that use various machine learning and filtering methods to propose useful items for users. Several techniques have been used to develop such a system for generating a list of useful recommendations. Traditionally, RSs use a single rating to represent preferences of a user on an item. A multi-criteria recommendation is a new technique that recommends items to users based on multiple attributes of the items. This technique has been used to solve many recommendation problems. Its predictive performance has been tested and proved to be more efficient than the traditional approach. However, this paper presents a model that is based on the architecture and main features of fuzzy sets and systems. Fuzzy logic (FL) is widely known for its application in different fields of study with its main advantage being that it does not need a lot of training data and its ability to combine human heuristics into the computer-assisted decision making process. FL is highly applicable in the domain of RS. The proposed study is to test and provide the predictive performance of the fuzzy-based multi-criteria technique and compare it with a single rating RS. Experimental results on real-world datasets from Yahoo! Movies proved that the proposed technique has remarkably improved the accuracy of the system