تشخیص تنظیمات کاربر بر اساس تجزیه و تحلیل شبکه های اجتماعی
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تشخیص تنظیمات کاربر بر اساس تجزیه و تحلیل شبکه های اجتماعی

عنوان فارسی مقاله: یک رویکرد برای تشخیص تنظیمات کاربر بر اساس تجزیه و تحلیل شبکه های اجتماعی
عنوان انگلیسی مقاله: An approach to identify user preferences based on social network analysis
مجله/کنفرانس: سیستم های کامپیوتری نسل آینده - Future Generation Computer Systems
رشته های تحصیلی مرتبط: مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: اینترنت و شبکه های گسترده، شبکه های کامپیوتری
کلمات کلیدی فارسی: محاسبات اجتماعی، تحلیل شبکه اجتماعی، تنظیمات کاربر، رویداد برنامه موبایل
کلمات کلیدی انگلیسی: Social computing، Social network analysis، User preferences، Mobile application event
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journal List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.future.2018.10.028
دانشگاه: Faculty of Organizational Sciences, University of Belgrade, Jove Ilića 154, Serbia
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 5/341 در سال 2017
شاخص H_index: 85 در سال 2019
شاخص SJR: 0/844 در سال 2017
شناسه ISSN: 0167-739X
شاخص Quartile (چارک): Q1 در سال 2017
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 9
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
کد محصول: E10963
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Theoretical background


3- Identifying user preferences using SNA


4- Results


5- Discussion and conclusion


References

نمونه متن انگلیسی مقاله

Abstract


This paper introduces an approach for identifying user preferences using social network analysis (SNA). Main idea was to reduce complexity and enable effective and affordable social network analysis harnessing particular tools and techniques. As a proof of concept, we performed the research that included two sources: (1) the control data source — analytical data collected from mobile application FilterApp for cultural events and (2) the experimental data source — data based on survey for users of the mobile application. The results revealed three clusters of cultural events based on user preferences towards certain types of cultural events, the frequency of visits to cultural events and the size of groups when visiting these events. The obtained conclusions were used to develop system of recommendations and for customization of offer and marketing strategies according to the identified users’ preferences. The main value of this paper is reflected in the clearly defined research process with all the phases from data collection to validation of results.


Introduction


Social network services include a plethora of personal, business and educational interactions among users. Data generated within social networks represents an input for social computing. The term social computing refers to an emerging concept that enables analysis of users’ profiles, as well as their interactions and behaviors [1]. Discovered information could be used in various contexts. For example, results of social computing research can lead to new targets and facilitate the process of introducing new products or services [2]. By introducing social computing online services, sharing of knowledge, experience, equipment, and resources can be increased among individuals, companies and research groups [3]. Social computing facilitates collective actions and social interactions by using various applications and services for multimedia content exchange and knowledge aggregation [4–6]. Social networks are the most explored part of social computing. Social Network Analysis (hereinafter: SNA) is the result of harnessing social computing in the analysis of social networks. SNA represents an interdisciplinary methodology for mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information entities [7]. SNA collects the data important for market research, making business decisions, analysis of marketing activities, identification of influential users of social networks, determining the interconnection of users of a particular social network, etc. This paper shows an approach to identifying user preferences based on social network analysis. The main aim of the paper is to investigate possibilities of harnessing SNA for determining users’ preferences in the context of cultural events. The paper should contribute to the literature by introducing an approach that enables SNA that is not dependent only on data from well known social networks, i.e could be performed on data from domain specific social network.

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