Harnessing the Power of User-Centric Artificial Intelligence: Customized Recommendations and Personalization in Hybrid Recommender Systems
Recommender systems are widely used in various fields, such as e-commerce, entertainment, and education, to provide personalized recommendations to users based on their preferences and/or behavior. Τhis paper presents a novel approach to providing customized recommendations with the use of user-cent...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/12/5/109 |
| _version_ | 1797600592088006656 |
|---|---|
| author | Christos Troussas Akrivi Krouska Antonios Koliarakis Cleo Sgouropoulou |
| author_facet | Christos Troussas Akrivi Krouska Antonios Koliarakis Cleo Sgouropoulou |
| author_sort | Christos Troussas |
| collection | DOAJ |
| description | Recommender systems are widely used in various fields, such as e-commerce, entertainment, and education, to provide personalized recommendations to users based on their preferences and/or behavior. Τhis paper presents a novel approach to providing customized recommendations with the use of user-centric artificial intelligence. In greater detail, we introduce an enhanced collaborative filtering (CF) approach in order to develop hybrid recommender systems that personalize search results for users. The proposed CF enhancement incorporates user actions beyond explicit ratings to collect data and alleviate the issue of sparse data, resulting in high-quality recommendations. As a testbed for our research, a web-based digital library, incorporating the proposed algorithm, has been developed. Examples of operation of the use of the system are presented using cognitive walkthrough inspection, which demonstrates the effectiveness of the approach in producing personalized recommendations and improving user experience. Thus, the hybrid recommender system, which is incorporated in the digital library, has been evaluated, yielding promising results. |
| first_indexed | 2024-03-11T03:50:02Z |
| format | Article |
| id | doaj.art-aef19d33b710410b83c8c275888ddd0c |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-11T03:50:02Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-aef19d33b710410b83c8c275888ddd0c2023-11-18T00:58:33ZengMDPI AGComputers2073-431X2023-05-0112510910.3390/computers12050109Harnessing the Power of User-Centric Artificial Intelligence: Customized Recommendations and Personalization in Hybrid Recommender SystemsChristos Troussas0Akrivi Krouska1Antonios Koliarakis2Cleo Sgouropoulou3Department of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, GreeceDepartment of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, GreeceDepartment of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, GreeceDepartment of Informatics and Computer Engineering, University of West Attica, 12243 Egaleo, GreeceRecommender systems are widely used in various fields, such as e-commerce, entertainment, and education, to provide personalized recommendations to users based on their preferences and/or behavior. Τhis paper presents a novel approach to providing customized recommendations with the use of user-centric artificial intelligence. In greater detail, we introduce an enhanced collaborative filtering (CF) approach in order to develop hybrid recommender systems that personalize search results for users. The proposed CF enhancement incorporates user actions beyond explicit ratings to collect data and alleviate the issue of sparse data, resulting in high-quality recommendations. As a testbed for our research, a web-based digital library, incorporating the proposed algorithm, has been developed. Examples of operation of the use of the system are presented using cognitive walkthrough inspection, which demonstrates the effectiveness of the approach in producing personalized recommendations and improving user experience. Thus, the hybrid recommender system, which is incorporated in the digital library, has been evaluated, yielding promising results.https://www.mdpi.com/2073-431X/12/5/109personalizationrecommender systemshybrid recommender systemscollaborative filteringdigital library |
| spellingShingle | Christos Troussas Akrivi Krouska Antonios Koliarakis Cleo Sgouropoulou Harnessing the Power of User-Centric Artificial Intelligence: Customized Recommendations and Personalization in Hybrid Recommender Systems Computers personalization recommender systems hybrid recommender systems collaborative filtering digital library |
| title | Harnessing the Power of User-Centric Artificial Intelligence: Customized Recommendations and Personalization in Hybrid Recommender Systems |
| title_full | Harnessing the Power of User-Centric Artificial Intelligence: Customized Recommendations and Personalization in Hybrid Recommender Systems |
| title_fullStr | Harnessing the Power of User-Centric Artificial Intelligence: Customized Recommendations and Personalization in Hybrid Recommender Systems |
| title_full_unstemmed | Harnessing the Power of User-Centric Artificial Intelligence: Customized Recommendations and Personalization in Hybrid Recommender Systems |
| title_short | Harnessing the Power of User-Centric Artificial Intelligence: Customized Recommendations and Personalization in Hybrid Recommender Systems |
| title_sort | harnessing the power of user centric artificial intelligence customized recommendations and personalization in hybrid recommender systems |
| topic | personalization recommender systems hybrid recommender systems collaborative filtering digital library |
| url | https://www.mdpi.com/2073-431X/12/5/109 |
| work_keys_str_mv | AT christostroussas harnessingthepowerofusercentricartificialintelligencecustomizedrecommendationsandpersonalizationinhybridrecommendersystems AT akrivikrouska harnessingthepowerofusercentricartificialintelligencecustomizedrecommendationsandpersonalizationinhybridrecommendersystems AT antonioskoliarakis harnessingthepowerofusercentricartificialintelligencecustomizedrecommendationsandpersonalizationinhybridrecommendersystems AT cleosgouropoulou harnessingthepowerofusercentricartificialintelligencecustomizedrecommendationsandpersonalizationinhybridrecommendersystems |