High-Performance Technique for Item Recommendation in Social Networks using Multiview Clustering

Abstract

Recommender Systems have been widely employed in information systems over the past few decades, making it easier for each user to choose their own products based on their past behaviour. Data mining tasks and visualization tools regularly use clustering techniques in the scientific and commercial arenas. It has been shown that clustering-based methods are effective and scalable to big data sets. The accuracy and coverage of clustering-based recommender systems are, however, somewhat low. In this paper, we suggest an improved multi-view clustering method for the recommendation of items in social networks to overcome these problems. To create better partitions, the artificial Bees colony optimization algorithm (ABC) is first used to improve the initial medoids’ selection. After that, users are clustered iteratively using views of both rating patterns as well as social information using multiview clustering (MVC) (i.e. trust and friendships). Ultimately, a framework is suggested for evaluating the various options. This research study suggests a novel MVC clustering approach using the ABC optimization technique. The proposed ABC-MVC algorithm’s usefulness in terms of enhancing accuracy is demonstrated by experimental findings performed on a real-world dataset and it is observed that it performs better than the pre-existing techniques and baselines.