Visible to the public A Computational Model for Improving the Accuracy of Multi-Criteria Recommender Systems

TitleA Computational Model for Improving the Accuracy of Multi-Criteria Recommender Systems
Publication TypeConference Paper
Year of Publication2017
AuthorsHassan, M., Hamada, M.
Conference Name2017 IEEE 11th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip (MCSoC)
ISBN Number978-1-5386-3441-7
KeywordsAdaptation models, adaptive self-organizing structures, aggregation function approach, Artificial neural networks, average similarity techniques, Biological neural networks, biological neurons, biologically inspired algorithms, Collaboration, collaborative filtering, computational model, Computational modeling, criteria ratings, highly distributed self-organizing structures, Human Behavior, human factors, Measurement, modeling approaches, Multi-criteria recommendation, multicriteria recommendations, multicriteria recommender systems, neural nets, Predictive models, pubcrawl, recommender systems, resilience, Resiliency, Scalability, similarity, single rating collaborative filtering technique, user modeling technique, user modelling

Artificial neural networks are complex biologically inspired algorithms made up of highly distributed, adaptive and self-organizing structures that make them suitable for optimization problems. They are made up of a group of interconnected nodes, similar to the great networks of neurons in the human brain. So far, artificial neural networks have not been applied to user modeling in multi-criteria recommender systems. This paper presents neural networks-based user modeling technique that exploits some of the characteristics of biological neurons for improving the accuracy of multi-criteria recommendations. The study was based upon the aggregation function approach that computes the overall rating as a function of the criteria ratings. The proposed technique was evaluated using different evaluation metrics, and the empirical results of the experiments were compared with that of the single rating-based collaborative filtering and two other similarity-based modeling approaches. The two similarity-based techniques used are: the worst-case and the average similarity techniques. The results of the comparative analysis have shown that the proposed technique is more efficient than the two similarity-based techniques and the single rating collaborative filtering technique.

Citation Keyhassan_computational_2017