Abstract
We often make decisions on the things we like, dislike, or even don’t care about. However, taking the right decisions becomes relatively difficult from a variety of items from different sources. Recommender systems are intelligent decision support software tools that help users to discover items that might be of interest to them. Various techniques and approaches have been applied to design and implement such systems to generate credible recommendations to users. A multi-criteria recommendation technique is an extended approach for modeling user’s preferences based on several characteristics of the items. This research presents genetic algorithm-based approaches for predicting user preferences in multi-criteria recommendation problems. Three genetic algorithms’ methods, namely standard genetic algorithm, adaptive genetic algorithm, and multi-heuristic genetic algorithms are used to conduct the experiments using a multi-criteria dataset for movies recommendation. The empirical results of the comparative analysis of their performance are presented in this study.
Highlights
We often make decisions on the things we like, dislike, or even don’t care about
The present study was designed to determine the effectiveness of various genetic algorithms techniques for improving the accuracy of Multicriteria RSs (MCRSs)
The two optimized GA-based techniques are the Adaptive genetic algorithm (AGA) that uses fitness values of the population to update the learning parameters and the Multi-heuristic genetic algorithm (MGA) technique that uses the concept of a simulated annealing algorithm to cool down the learning parameters to avoid premature convergence
Summary
We often make decisions on the things we like, dislike, or even don’t care about. taking the right decisions becomes relatively difficult from a variety of items from different sources. Niques which use multiple ratings from various characteristics of items to model users’ preferences and make more accurate and effective recommendations This is because different users may have different tastes on items subject to numerous features of the items. The aggregation function approach has been used in different ways by many researchers such as Adomavicius & Kwon 2, Teng and Lee 42, Lakiotaki et al 32, and most recently by Jannach et al 27 26 who used support vector regression to model multi-criteria recommendation problems. Some of these approaches have some weaknesses. More research on modeling multicriteria recommendation problems are required to improve the accuracy of the systems
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Computational Intelligence Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
