Sistemi preporuke u e-trgovini

Simović, Aleksandar

doi:10.15308/sinteza-2014-846-852

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…At any new access by the user to the online bookstore, the system analyzes the users’ purchase history, attributes, and personalized preferences to generate a list of recommendations and content of interest. The item-based CF compares each purchase made by users, ranks the items by similarity, and by combining the similar products generates a recommendation (Simović, 2014). To every user who has purchased or rated items from the University online bookstore, the system recommends three books. Step 6: at a user’s new logon the LMS Moodle platform, the proposed Big Data recommendation system processes the data from the University library, the IS of the educational institution, the online bookstore server logs, and the LMS platform to generate a recommendation.…”

Section: Implementation and Resultsmentioning

confidence: 99%

Section: Implementation and Resultsmentioning

confidence: 99%

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A Big Data smart library recommender system for an educational institution

Simović

2018

LHT

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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.

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Section: Implementation and Resultsmentioning

confidence: 99%

Section: Implementation and Resultsmentioning

confidence: 99%