A Big Data smart library recommender system for an educational institution

Abstract

Purpose With the exponential growth of the amount of data, the most sophisticated systems of traditional libraries are not able to fulfill the demands of modern business and user needs. The purpose of this paper is to present the possibility of creating a Big Data smart library as an integral and enhanced part of the educational system that will improve user service and increase motivation in the continuous learning process through content-aware recommendations. Design/methodology/approach This paper presents an approach to the design of a Big Data system for collecting, analyzing, processing and visualizing data from different sources to a smart library specifically suitable for application in educational institutions. Findings As an integrated recommender system of the educational institution, the practical application of Big Data smart library meets the user needs and assists in finding personalized content from several sources, resulting in economic benefits for the institution and user long-term satisfaction. Social implications The need for continuous education alters business processes in libraries with requirements to adopt new technologies, business demands, and interactions with users. To be able to engage in a new era of business in the Big Data environment, librarians need to modernize their infrastructure for data collection, data analysis, and data visualization. Originality/value A unique value of this paper is its perspective of the implementation of a Big Data solution for smart libraries as a part of a continuous learning process, with the aim to improve the results of library operations by integrating traditional systems with Big Data technology. The paper presents a Big Data smart library system that has the potential to create new values and data-driven decisions by incorporating multiple sources of differential data.

Big Data: Opportunities and Challenges in Libraries, a Systematic Literature Review

“…82 Simović presented a Big Data smart library system that has the potential to create new values and data-driven decisions by incorporating multiple sources of differential data. 83 Ye presented a technology framework that included the distributed storage and parallel computing model of mass data, the distributed management model of diverse data sources, and the model of diversified service application for university libraries. 84 Zheng et al considered university libraries as important supports for teaching and researching in the era of Big Data.…”

From Electronic to Smart Library Systems: Concept, Classification of Services, Scheme of Work and Model

“…The smart library concept has been discussed extensively in previous research, mainly by Western scholars, including Cao et al (2018), Johnson (2013), Sreekumar (2012), Zimmerman et al (2018), Simović (2018), Leorke et al (2018), Bajic et al (2008), Burgess (2010), Dean et al (2010) and Pan (2010). The Russian pool of works is represented by a limited number of researchers, such as , Vasilieva (2016) and the works of researchers from the Siberian Federal University including Noskov et al (2017) and Baryshev et al (2014Baryshev et al ( , 2018.…”

A Systematic Study on the Recommender Systems in the E-Commerce

“…Ecommerce websites, such as eBay, Alibaba, Amazon, and Netflix, provide great opportunities for their users. These ecommerce platforms tend to utilize the social network features [7], expert cloud technology [8][9][10][11][12], big data [13], and using mobile devices to provide user satisfaction for their users. The Recommender Systems (RSs) learn the experiences and opinions from their customers' behaviors and recommend the items or products that they will find the most relevant among the possible results.…”