A Fuzzy-Based Approach for Modelling Preferences of Users in Multi-Criteria Recommender Systems
Recommender systems are web-based platforms or software that use various machine learning methods to propose useful items to users. Several techniques have been used to develop such a system for generating a list of recommendations. Multi-criteria is a new technique that recommends items based on multiple characteristics or attributes of the items. This technique has been used to solve many recommendation problems and its predictive performance has been tested and proven to be more effective than the traditional approach. However, current research has shown that there is still a need to use some machine learning techniques in modelling the criteria ratings in multi-criteria recommendation techniques. The proposed project aimed to present a model that is based on the architecture and main features of fuzzy sets and systems. Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning. It is one of the machine learning techniques that is widely known for its effective application in different fields of study. Its main advantage is that it does not need a lot of data to train, coupled with its ability to combine human heuristics into computer-assisted decision making, which is highly applicable in the domain of recommender systems. The proposed project is designed to test and provide the predictive performance of the fuzzy-based multi-criteria technique and compare it with some of the existing methods. The main focus of this research is to model a system that can optimize the prediction accuracy of an RS, increase in ranking accuracy, and thus obtain high correlation between the predicted and actual values. Experimental results performed on real-world datasets (Yahoo movies) proved that the proposed technique (Fuzzy Multi-criteria Recommender System) remarkably improved the accuracy of prediction in multi-criteria CF RS. The system was implemented using java programming language.