Next Article in Journal
Sphere Fitting with Applications to Machine Tracking
Previous Article in Journal
Equivalence of the Frame and Halting Problems
Previous Article in Special Issue
An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings

Towards Cognitive Recommender Systems

Department of Computing, Macquarie University, Macquarie Park 2109, Australia
Author to whom correspondence should be addressed.
Algorithms 2020, 13(8), 176;
Received: 1 May 2020 / Revised: 20 June 2020 / Accepted: 16 July 2020 / Published: 22 July 2020
(This article belongs to the Special Issue Algorithms for Personalization Techniques and 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 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, Cognitive Recommender Systems. 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. View Full-Text
Keywords: recommender systems; cognitive technology; deep learning; knowledge lakes recommender systems; cognitive technology; deep learning; knowledge lakes
Show Figures

Figure 1

MDPI and ACS Style

Beheshti, A.; Yakhchi, S.; Mousaeirad, S.; Ghafari, S.M.; Goluguri, S.R.; Edrisi, M.A. Towards Cognitive Recommender Systems. Algorithms 2020, 13, 176.

AMA Style

Beheshti A, Yakhchi S, Mousaeirad S, Ghafari SM, Goluguri SR, Edrisi MA. Towards Cognitive Recommender Systems. Algorithms. 2020; 13(8):176.

Chicago/Turabian Style

Beheshti, Amin, Shahpar Yakhchi, Salman Mousaeirad, Seyed M. Ghafari, Srinivasa R. Goluguri, and Mohammad A. Edrisi. 2020. "Towards Cognitive Recommender Systems" Algorithms 13, no. 8: 176.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop