Kernel Context Recommender System (KCR): A Scalable Context-Aware Recommender System Algorithm
Article
Iqbal, Misbah, Ghazanfar, M., Sattar, Asma, Maqsood, Muazzam, Khan, Salabat, Mehmood, Irfan and Baik, Sung Wook 2019. Kernel Context Recommender System (KCR): A Scalable Context-Aware Recommender System Algorithm. IEEE Access. 7, pp. 24719-24737. https://doi.org/10.1109/ACCESS.2019.2897003
Authors | Iqbal, Misbah, Ghazanfar, M., Sattar, Asma, Maqsood, Muazzam, Khan, Salabat, Mehmood, Irfan and Baik, Sung Wook |
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Abstract | Recommender systems are intelligent data mining applications that deal with the issue of information overload significantly. The available literature discusses several methodologies to generate recommendations and proposes different techniques in accordance with users’ needs. The majority of the work in the recommender system domain focuses on increasing the recommendation accuracy by employing several proposed approaches where the main motive remains to maximize the accuracy of recommendations while ignoring other design objectives, such as a user’s an item’s context. The biggest challenge for a recommender system is to produce meaningful recommendations by using contextual user-item rating information. A context is a vast term that may consider various aspects; for example, a user’s social circle, time, mood, location, weather, company, day type, an item’s genre, location, and language. Typically, the rating behavior of users varies under different contexts. From this line of research, we have proposed a new algorithm, namely Kernel Context Recommender System, which is a flexible, fast, and accurate kernel mapping framework that recognizes the importance of context and incorporates the contextual information using kernel trick while making predictions. We have benchmarked our proposed algorithm with pre- and post-filtering approaches as they have been the favorite approaches in the literature to solve the context-aware recommendation problem. Our experiments reveal that considering the contextual information can increase the performance of a system and provide better, relevant, and meaningful results on various evaluation metrics. |
Journal | IEEE Access |
Journal citation | 7, pp. 24719-24737 |
ISSN | 2169-3536 |
Year | 2019 |
Publisher | IEEE |
Publisher's version | License |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2019.2897003 |
Web address (URL) | https://doi.org/10.1109/ACCESS.2019.2897003 |
Publication dates | |
04 Feb 2019 | |
Publication process dates | |
Deposited | 28 Mar 2019 |
Accepted | 16 Jan 2019 |
Accepted | 16 Jan 2019 |
Funder | Ministry of Science ICT and Future Planning |
Ministry of Science ICT and Future Planning | |
National Research Foundation of Korea | |
Copyright information | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
License | All rights reserved |
https://repository.uel.ac.uk/item/844vv
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