 RECOMMENDER SYSTEM’S ECONOMIC IMPACT ON EBUSINESS. A THEORETICAL REVIEWRadu-Adrian Marincean ()
Annals of Faculty of Economics, 2023, vol. 32, issue 1, 728-741 Abstract: The rapid advancement of technology and the Internet led to an unprecedented abundance of information and data. Where too much information exists, the risk raises for that information to become irrelevant or too hard to handle; a phenomenon called information overload. Filtering vasts amounts of data and highlighting relevant information became a priority, especially for ecommerce business. Recommender Systems (RS) as a branch of Decision Support Systems were developed and implemented to help users handle information overload and access items based on relevancy. The financial returns RS have brought stimulated the spread of such referral systems to other business domains. As knowledge is a critical resource in nowadays economy, efficient knowledge production and management are a prerequisite for competitive advantage. E-businesses are concerned with online traffic on their platforms and with customer experience and impressions. The multitude of e-businesses facilitated by the Internet has created a highly competitive market in terms of gaining customers loyalty. New available frontier technologies might help online retailers enhance their customer pool and build a solid relationship with their existing ones. One major issue RS tackle is the information overload, meaning that vasts amounts of data might confuse the customer in making a purchase choice, paradoxically due to too many options. Information overload might lead to fatigue, purchase postpone and overall loss for the online retailers. RS have the power to gather data and transform it to valuable personalized knowledge; a feature that can add more revenue, build customer trust, build a personalized customer relationship and even influence the distribution value chain. In this paper we propose a theoretical overview on the RS and how they create value, their fields of implementation and how they are working. By doing so, we enhance both the RS and the e-commerce literature by analyzing tools and means of economic development provided by the DT. Keywords: recommender-systems; decision support systems; e-commerce; content-based filtering; information-overload. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ora:journl:v:32:y:2023:i:1:p:728-741 Access Statistics for this article More articles in Annals of Faculty of Economics from University of Oradea, Faculty of Economics Contact information at EDIRC. |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.