Harnessing the Power of User-Centric Artificial Intelligence: Customized Recommendations and Personalization in Hybrid Recommender Systems

Troussas, Christos; Krouska, Akrivi; Koliarakis, Antonios; Sgouropoulou, Cleo

doi:10.3390/computers12050109

Cited by 9 publications

(3 citation statements)

References 30 publications

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“…This analysis goes beyond surfacelevel understanding, diving deep into the context in which users will interact with the AI-powered solution. Empathizing with users allows businesses to design AI-powered solutions that are not only technically proficient but also intuitive and user-friendly, enhancing the overall user experience [26,27]. Creating detailed user personas helps visualize the target audience.…”

Section: (B) Encouraging Creative Problem-solvingmentioning

confidence: 99%

AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact

Aldoseri,

Al-Khalifa,

Hamouda

2024

Sustainability

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Digital transformation systems generate a substantial volume of data, creating opportunities for potential innovation, particularly those driven by artificial intelligence. This study focuses on the intricate relationship between artificial intelligence and innovation as foundational elements in the digital transformation framework for sustained growth and operational excellence. This study provides a holistic perspective on the cultivation and pillars of AI-powered innovation, highlighting their pivotal role in revolutionizing industries, including healthcare, education, finance, manufacturing, transportation, and agriculture. The work emphasizes the key pillars essential for fostering AI-powered innovation, including monitoring performance measurement to use the power of the present, continuous learning and innovation, data analytics and insights, predictive analytics, and innovative product development. This study investigates how these pillars serve as the foundation for groundbreaking advancements, driving efficiency, enhancing decision-making processes, and fostering creativity within organizations. This study explores the significance of continuous learning, interdisciplinary collaboration, and industry partnerships in nurturing a thriving AI-powered innovation ecosystem. By understanding and harnessing these fundamental elements, businesses can navigate the complexities of the digital age, fostering innovation that not only optimizes processes but also enhances the overall human experience, ushering in a new era of technological excellence and societal progress.

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Section: (B) Encouraging Creative Problem-solvingmentioning

confidence: 99%

AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact

Aldoseri,

Al-Khalifa,

Hamouda

2024

Sustainability

View full text Add to dashboard Cite

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“…By merging numerous recommendations approaches, hybrid recommender systems are essential to improving the performance of recommendation systems [25,26,27]. Hybridization's primary objective is to overcome the shortcomings of each individual recommender system and enhance the system as a whole [28,29,30,31]. The literature has a variety of hybridization techniques, including weighted, switching, mixed, feature combination, cascade, feature improvement, and meta-level approaches [32].…”

Section: Hybrid Recommender Systemsmentioning

confidence: 99%

OntoCommerce: Incorporating Ontology and Sequential Pattern Mining for Personalized E-Commerce Recommendations

Mustafa,

Jhamat,

Arshad

et al. 2024

IEEE Access

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The abundance of information on online purchasing websites makes it challenging for customers to locate products that match their preferences. However, the cold-start problem arises when there isn't enough previous data, making it harder to make accurate recommendations for new customers or products. The enormous number of possible customers and products in a recommendation system leads to sparse data, which makes it harder to generate relevant recommendations and causes the sparsity problem. In addition, existing e-commerce recommender systems have difficulty making accurate product recommendations because they disregard individual consumer characteristics. In order to overcome these limitations, a hybrid recommender system combining ontology and sequential pattern mining (SPM) techniques is proposed. The strategy entails constructing an ontology that encompasses customer and product-related knowledge in the e-commerce domain. This ontology is then utilized to calculate customer preference similarities and generate predictions for the intended customer. The SPM algorithm is applied to the results of collaborative filtering to generate personalized recommendations for the customer. Experiments have demonstrated that the hybrid recommender system outperforms existing methods and resolves the cold-start problem and data sparsity in e-commerce recommender systems effectively. Even with limited initial data, the system generates accurate and individualized recommendations based on ontological domain knowledge and the customer's sequential purchase patterns. By integrating ontology and sequential pattern mining, this strategy enhances the precision and individualization of the e-commerce industry's recommendation process.

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“…Hybrid recommendation systems combine content-based and collaborative filtering approaches to leverage the strengths of both methods to provide more accurate, diverse, and adaptable recommendations while addressing the limitations of each individual approach. Hybrid recommendation systems offer certain advantages: (1) more accurate recommendations, (2) overcome data sparsity, (3) more diverse recommendations, (4) mitigate the cold-start problem, and (5) flexibility and adaptability [44,45].…”

Section: Hybrid Recommendation Systemsmentioning

confidence: 99%

Developing an Intelligent Recommendation System for Non-Information and Communications Technology Major University Students

Kim,

Lim

2023

Applied Sciences

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Various services and applications based on information and communications technology (ICT) are converging with cultural aspects of historical implementations. At the same time, developing a convergence course for non-ICT majors is becoming increasingly popular in universities. In this paper, we develop an AI application course for non-ICT major university students toward convergence with recommendation systems and Silk Road studies. Based on our five-year research on the martial arts, dance, and play of seven Silk Road countries, we have created and categorized an accessible database for 177 items in those countries. For our convergence course, we measure the similarity between the items for summary and perform collaborative filtering based on alternating least squares (ALS) matrix factorization so that our prototyped intelligent recommendation engine can predict the items in which a user might be interested. The course is designed to teach non-ICT major university students not only historical aspects of the Silk Road but also implementation aspects of recommendation systems with web services.

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Harnessing the Power of User-Centric Artificial Intelligence: Customized Recommendations and Personalization in Hybrid Recommender Systems

Cited by 9 publications

References 30 publications

AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact

AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact

OntoCommerce: Incorporating Ontology and Sequential Pattern Mining for Personalized E-Commerce Recommendations

Developing an Intelligent Recommendation System for Non-Information and Communications Technology Major University Students

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