Group Recommender Systems – An Evolutionary Approach Based on Multi-expert System for Consensus

Meena, Ritu; Minz, Sonajharia

doi:10.1515/jisys-2018-0081

Cited by 10 publications

(3 citation statements)

References 38 publications

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“…A genetic algorithm based multi expert scheme (GA) MES-GRS-GA is presented in [11], which discuss consensus scheme towards group recommendation system. A predictive system is designed towards supporting cardiac disease in [12], which analyzes various machine learning algorithms and their results.…”

Section: Related Workmentioning

confidence: 99%

Multi Feature DCR based Drug Compound Selection and Recommendation System for Efficient Decision-Making using Genetic Algorithm

Aarthy¹,

Iqbal²

2023

IJACSA

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The performance of treating the cardiac diseases is dependent on the kind of drug being selected. There exist numerous decisive support systems which work according to certain characteristics and factors like drug availability, and popularity. Still, they struggle to achieve expected performance in supporting the medical practitioner. To handle this issue, a multi feature drug curing rate based drug compound selection and recommendation system (MDCRSR) is presented. The method utilizes medical histories and data set of various medical organization around the disease considered. Using the traces, the method identifies the drug compounds and features to perform preprocessing which eliminates the noisy data points. Further, the features of the traces are extracted to perform training with genetic algorithm. At the test phase, the method estimates the fitness measure for different drug combination and compounds by measuring their Drug Curing Rate (DCR). The method performs cross over and mutation to produce various populations of drug compounds. According to the curing rate, the drug compound pattern or population is selected and ranked. The ranked results are populated to the medical practitioner. The method improves the performance of recommendation system as well as drug compound selection.

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Section: Related Workmentioning

confidence: 99%

Multi Feature DCR based Drug Compound Selection and Recommendation System for Efficient Decision-Making using Genetic Algorithm

Aarthy¹,

Iqbal²

2023

IJACSA

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“…There is an urgent need to develop practical group recommender systems [3,6,50]. The group recommendation aims to reach a consensus among group members, generating suggestions that can cater to most group members [21,29]. However, the influences of users vary from group to group, leading to hard decision-making.…”

Section: Introductionmentioning

confidence: 99%

Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation

Zhang

Gao

et al. 2021

Proceedings of the 30th ACM International Conference on Information &Amp; Knowledge Management

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With the prevalence of social media, there has recently been a proliferation of recommenders that shift their focus from individual modeling to group recommendation. Since the group preference is a mixture of various predilections from group members, the fundamental challenge of group recommendation is to model the correlations among members. Existing methods mostly adopt heuristic or attention-based preference aggregation strategies to synthesize group preferences. However, these models mainly focus on the pairwise connections of users and ignore the complex high-order interactions within and beyond groups. Besides, group recommendation suffers seriously from the problem of data sparsity due to severely sparse group-item interactions. In this paper, we propose a self-supervised hypergraph learning framework for group recommendation to achieve two goals: (1) capturing the intraand inter-group interactions among users; (2) alleviating the data sparsity issue with the raw data itself. Technically, for (1), a hierarchical hypergraph convolutional network based on the userand group-level hypergraphs is developed to model the complex tuplewise correlations among users within and beyond groups. For (2), we design a double-scale node dropout strategy to create selfsupervision signals that can regularize user representations with different granularities against the sparsity issue. The experimental analysis on multiple benchmark datasets demonstrates the superiority of the proposed model and also elucidates the rationality of the hypergraph modeling and the double-scale self-supervision. CCS CONCEPTS• Information systems → Recommender systems.

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“…Os princípios básicos para a elaboração de um algoritmo genético são apresentados a seguir (MEENA; MINZ, 2019;KUMAR et al, 2010):…”

Section: Algoritmo Genético Para Consensounclassified

Proposta de um modelo híbrido de tomada de decisão em grupo baseado na teoria >i<dual hesitant fuzzy>/i< e algoritmo genético

Calache¹

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Group Recommender Systems – An Evolutionary Approach Based on Multi-expert System for Consensus

Cited by 10 publications

References 38 publications

Multi Feature DCR based Drug Compound Selection and Recommendation System for Efficient Decision-Making using Genetic Algorithm

Multi Feature DCR based Drug Compound Selection and Recommendation System for Efficient Decision-Making using Genetic Algorithm

Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation

Proposta de um modelo híbrido de tomada de decisão em grupo baseado na teoria >i<dual hesitant fuzzy>/i< e algoritmo genético

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