Towards Cognitive Recommender Systems

Autor: Mohammad Amin Edrisi, Shahpar Yakhchi, Amin Beheshti, Srinivasa Reddy Goluguri, Seyed Mohssen Ghafari, Salman Mousaeirad
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Algorithms
Volume 13
Issue 8
Algorithms, Vol 13, Iss 176, p 176 (2020)
ISSN: 1999-4893
DOI: 10.3390/a13080176
Popis: Intelligence is the ability to learn from experience and use domain experts&rsquo
knowledge to adapt to new situations. In this context, an intelligent Recommender System should be able to learn from domain experts&rsquo
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&rsquo
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&rsquo
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, Cognitive Recommender Systems. Cognitive Recommender Systems will be the new type of intelligent Recommender Systems that understand the user&rsquo
s preferences, detect changes in user preferences over time, predict user&rsquo
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&rsquo
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&rsquo
cognitive activities and use it to provide intelligent and time-aware recommendations.
Databáze: OpenAIRE
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