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.
AuthorsIqbal, Misbah, Ghazanfar, M., Sattar, Asma, Maqsood, Muazzam, Khan, Salabat, Mehmood, Irfan and Baik, Sung Wook
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.

JournalIEEE Access
Journal citation7, pp. 24719-24737
ISSN2169-3536
Year2019
PublisherIEEE
Publisher's version
License
Digital Object Identifier (DOI)doi:10.1109/ACCESS.2019.2897003
Web address (URL)https://doi.org/10.1109/ACCESS.2019.2897003
Publication dates
Print04 Feb 2019
Publication process dates
Deposited28 Mar 2019
Accepted16 Jan 2019
Accepted16 Jan 2019
FunderMinistry of Science ICT and Future Planning
Ministry of Science ICT and Future Planning
National Research Foundation of Korea
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LicenseAll rights reserved
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