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  • articleNo Access

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

    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.

  • chapterNo Access

    A Novel Approach for Measuring Demographic Parity Fairness in Group Recommendation

    Fairness is currently becoming a necessary dimension to consider in contemporary artificial intelligence (AI)-based systems, according to the recent Ethics Guidelines for a Trustworthy AI. Being recommender systems popular applications that incorporate AI to a larger or lesser extent, the literature analysis identifies a research gap related to the exploration of demographic parity fairness in the group recommendation scenario. This chapter focuses on this gap, developing a group recommendation framework that has as main novelty the measuring of the consumer fairness taking into account the presence of advantaged and disadvantaged classes of users. Experimental studies are developed for measuring the performance of the proposal in a real recommendations scenario, illustrating that it is able to distinguish different fairness levels across the delivered recommendations.

  • chapterNo Access

    On group recommendation supported by a minimum cost consensus model

    Group recommender systems (GRSs) have recently attracted the attention from researchers and industry. They focused on recommending items which satisfy the global preferences of a group, being TV programs and holidays packages typical examples of these scenarios. Although there have been established several basic approaches for GRSs, it has been also identified the limitation about dealing with conflicts about the recommendation within the groups and hence, the necessity of managing in a deeper way the consensus among the group members to improve the agreed satisfaction of the recommendations. The current contribution is focused on proposing the application of the minimum cost consensus model in the GRS scenario for achieving such objective. A case study will show that this consensus model positively influences the groups’ satisfaction about the recommendations.