Towards Cognitive Recommender Systems
Intelligence is the ability to learn from experience and use domain experts’ knowledge to adapt to new situations. In this context, an intelligent Recommender System should be able to learn from domain experts’ knowledge and experience, as it is vital to know the domain that the items will be recomm...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Algorithms |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4893/13/8/176 |
| _version_ | 1797561752907415552 |
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| author | Amin Beheshti Shahpar Yakhchi Salman Mousaeirad Seyed Mohssen Ghafari Srinivasa Reddy Goluguri Mohammad Amin Edrisi |
| author_facet | Amin Beheshti Shahpar Yakhchi Salman Mousaeirad Seyed Mohssen Ghafari Srinivasa Reddy Goluguri Mohammad Amin Edrisi |
| author_sort | Amin Beheshti |
| collection | DOAJ |
| description | Intelligence is the ability to learn from experience and use domain experts’ knowledge to adapt to new situations. In this context, an intelligent Recommender System should be able to learn from domain experts’ knowledge and experience, as it is vital to know the domain that the items will be recommended. Traditionally, Recommender Systems have been recognized as playlist generators for video/music services (e.g., Netflix and Spotify), e-commerce product recommenders (e.g., Amazon and eBay), or social content recommenders (e.g., Facebook and Twitter). However, Recommender Systems in modern enterprises are highly data-/knowledge-driven and may rely on users’ cognitive aspects such as personality, behavior, and attitude. In this paper, we survey and summarize previously published studies on Recommender Systems to help readers understand our method’s contributions to the field in this context. We discuss the current limitations of the state of the art approaches in Recommender Systems and the need for our new approach: A vision and a general framework for a new type of data-driven, knowledge-driven, and cognition-driven Recommender Systems, namely, <i>Cognitive Recommender Systems</i>. Cognitive Recommender Systems will be the new type of intelligent Recommender Systems that understand the user’s preferences, detect changes in user preferences over time, predict user’s unknown favorites, and explore adaptive mechanisms to enable intelligent actions within the compound and changing environments. We present a motivating scenario in banking and argue that existing Recommender Systems: (i) do not use domain experts’ knowledge to adapt to new situations; (ii) may not be able to predict the ratings or preferences a customer would give to a product (e.g., loan, deposit, or trust service); and (iii) do not support data capture and analytics around customers’ cognitive activities and use it to provide intelligent and time-aware recommendations. |
| first_indexed | 2024-03-10T18:19:02Z |
| format | Article |
| id | doaj.art-93cc5f3e0ca64a8fadfde9cd3abbfdcd |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T18:19:02Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-93cc5f3e0ca64a8fadfde9cd3abbfdcd2023-11-20T07:30:38ZengMDPI AGAlgorithms1999-48932020-07-0113817610.3390/a13080176Towards Cognitive Recommender SystemsAmin Beheshti0Shahpar Yakhchi1Salman Mousaeirad2Seyed Mohssen Ghafari3Srinivasa Reddy Goluguri4Mohammad Amin Edrisi5Department of Computing, Macquarie University, Macquarie Park 2109, AustraliaDepartment of Computing, Macquarie University, Macquarie Park 2109, AustraliaDepartment of Computing, Macquarie University, Macquarie Park 2109, AustraliaDepartment of Computing, Macquarie University, Macquarie Park 2109, AustraliaDepartment of Computing, Macquarie University, Macquarie Park 2109, AustraliaDepartment of Computing, Macquarie University, Macquarie Park 2109, AustraliaIntelligence is the ability to learn from experience and use domain experts’ knowledge to adapt to new situations. In this context, an intelligent Recommender System should be able to learn from domain experts’ knowledge and experience, as it is vital to know the domain that the items will be recommended. Traditionally, Recommender Systems have been recognized as playlist generators for video/music services (e.g., Netflix and Spotify), e-commerce product recommenders (e.g., Amazon and eBay), or social content recommenders (e.g., Facebook and Twitter). However, Recommender Systems in modern enterprises are highly data-/knowledge-driven and may rely on users’ cognitive aspects such as personality, behavior, and attitude. In this paper, we survey and summarize previously published studies on Recommender Systems to help readers understand our method’s contributions to the field in this context. We discuss the current limitations of the state of the art approaches in Recommender Systems and the need for our new approach: A vision and a general framework for a new type of data-driven, knowledge-driven, and cognition-driven Recommender Systems, namely, <i>Cognitive Recommender Systems</i>. Cognitive Recommender Systems will be the new type of intelligent Recommender Systems that understand the user’s preferences, detect changes in user preferences over time, predict user’s unknown favorites, and explore adaptive mechanisms to enable intelligent actions within the compound and changing environments. We present a motivating scenario in banking and argue that existing Recommender Systems: (i) do not use domain experts’ knowledge to adapt to new situations; (ii) may not be able to predict the ratings or preferences a customer would give to a product (e.g., loan, deposit, or trust service); and (iii) do not support data capture and analytics around customers’ cognitive activities and use it to provide intelligent and time-aware recommendations.https://www.mdpi.com/1999-4893/13/8/176recommender systemscognitive technologydeep learningknowledge lakes |
| spellingShingle | Amin Beheshti Shahpar Yakhchi Salman Mousaeirad Seyed Mohssen Ghafari Srinivasa Reddy Goluguri Mohammad Amin Edrisi Towards Cognitive Recommender Systems Algorithms recommender systems cognitive technology deep learning knowledge lakes |
| title | Towards Cognitive Recommender Systems |
| title_full | Towards Cognitive Recommender Systems |
| title_fullStr | Towards Cognitive Recommender Systems |
| title_full_unstemmed | Towards Cognitive Recommender Systems |
| title_short | Towards Cognitive Recommender Systems |
| title_sort | towards cognitive recommender systems |
| topic | recommender systems cognitive technology deep learning knowledge lakes |
| url | https://www.mdpi.com/1999-4893/13/8/176 |
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