Enhancing Prediction Accuracy of a Multi-Criteria Recommender System using Adaptive Genetic Algorithm
Recommender systems are powerful intelligent systems considered to be the solution to the problems of information overload. They provide users with personalized lists of recommended items, using some machine learning techniques. Traditionally, existing recommender systems have used single rating techniques to estimate users' opinions on items. Because user preferences might depend on the attributes of several items, the efficiency of traditional single-rating recommender systems is considered to be limited, since they cannot account for various items' attributes. A multi-criteria recommendation is a new technique that uses ratings of various items' attributes to make more efficient predictions. Nevertheless, despite the proven accuracy improvements of multi-criteria recommendation techniques, research needs to be done continuously to establish an efficient way to model criteria ratings. This project, therefore, propose to use an adaptive genetic algorithm to model multi-criteria recommendation problems, using an aggregation function approach. The anticipated empirical results of the project show that the proposed approach provides more accurate predictions than traditional recommendation approaches.