An Improved Cat Swarm Search-Based Deep Ensemble Learning Model for Group Recommender Systems

Abstract

Recommender systems are often employed in different fields such as music, travel, and movies. The recommender systems are broadly utilised nowadays due to the emergence of social activities, in which particular recommendations are offered by group recommender systems. It is a system for recommending the items to a set of users together based on their preferences. The user preferences are used from the behavioural and social aspects of group members to enhance the quality of products recommended in various groups for generating the group recommendations. These group recommender systems solve the cold start problem, which is raised in an individual recommendation system. The ultimate aim of this paper is to design and develop a new Improved Deep Ensemble Learning Model (ID-ELM) for the group recommender systems concerning different application-oriented datasets. Initially, the datasets from different applications like healthcare, e-commerce, and e-learning are gathered from benchmark sources and split the data into various groups. These data are given to the pre-processing for making it fit for further processing. The pre-processing steps like stop word removal, stemming, and punctuation removal are performed here. Then the features are extracted using the Continuous Bag of Words Model (CBOW), and Principal Component Analysis (PCA) is used for dimension reduction. These features are fed to the ID-ELM, in which the optimised Convolutional Neural Network (CNN) extracts the significant features from the pooling layer, and the fully connected layer is replaced by a set of classifiers termed Neural Networks (NN), AdaBoost, and Logistic Regression (LR). Finally, the ranking of the ensemble learning model based on the group reviews extends the recommendation outcome. The optimised CNN is proposed by the Adaptive Seeking Range-based Cat Swarm Optimisation (ASR-CSO) for attaining better results. This model is validated on the benchmark datasets to show the efficiency of the designed model with different meta-heuristic-based algorithms and classification algorithms.