Personalised Recommendation Systems and the Impact of COVID-19: Perspectives, Opportunities and Challenges

Rabaa Abdulrahman, Herna Viktor

Abstract

Personalised Recommendation Systems that utilize machine learning algorithms have had much success in recent years, leading to accurate predictions in many e-business domains. However, this environment experienced abrupt changes with the onset of the COVID-19 pandemic centred on an exponential increase in the volume of customers and swift alterations in customer behaviours and profiles. This position paper discusses the impact of the COVID-19 pandemic on the Recommendation Systems landscape and focuses on new and atypical users. We detail how online machine learning algorithms that are able to detect and subsequently adapt to changes in consumer behaviours and profiles can be used to provide accurate and timely predictions regarding this evolving consumer sector.

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