مدل‌سازی رفتار بازنشر کاربران در اجتماعات برخط با استفاده از تیمی از اتوماتاهای یادگیر

نوع مقاله : مقاله کامپیوتر

نویسندگان

دانشکده مهندسی کامپیوتر و فناوری اطلاعات، دانشگاه صنعتی امیرکبیر.

10.22075/jme.2019.17197.1689

چکیده

امروزه اجتماعات برخط، در گردش اطلاعات نظیر اخبار، محتوای آموزشی، مطالب سرگرم‌کننده و غیره نقش مهمی دارند. میلیون‌ها کاربر به صورت روزانه پست‌های مختلف را در این محیط، ایجاد می‌نمایند. کاربران در صورت تمایل، برخی از پست را بازنشر می‌کنند. بازنشر یک پست در انتقال اطلاعات بین کاربران تاثیر اساسی دارد. با توجه به تعداد زیاد پست‌ها، کاربران در این گونه اجتماعات با مشکل گرانباری اطلاعات مواجه هستند. این مسئله موجب کاهش بازنشر پست‌ها و اختلال در انتقال اطلاعات در اجتماعات برخط شده است. در این مقاله، رفتار بازنشر کاربران در مواجهه با پست‌های مختلف، مدل‌سازی شده است. برای این کار در ابتدا عوامل مؤثر در رفتار بازنشر کاربران شناسایی شده است و سپس با استفاده از یک روش یادگیری تقویتی، رفتار کاربران در مواجهه با یک پست پیش‌بینی شده است. این روش یادگیری تقویتی به صورت یک بازی برای تیمی از اتوماتاهای یادگیر تصادفی طراحی شده است. برای ارزیابی، از سه مجموعه داده نسبتا بزرگ استفاده شده است. بر اساس نتایج بدست آمده، اتوماتاهای یادگیر تصادفی با توجه به ویژگی‌های محیط و قدرت یادگیری برخط، قابلیت اجرایی بسیار خوبی داشته است.

کلیدواژه‌ها


عنوان مقاله [English]

Modeling Users’ Repost Behavior in Online Communities Using a Team of Learning Automata

نویسندگان [English]

  • omid reza Bolouki Speily
  • Ahmad Kardan
Department of Computer Engineering & Information Technology, AmirKabir University of Technology
چکیده [English]

Today's online communities play an important role in the flow of information such as news, educational contents, entertainment, and so on. Millions of users create different posts in this environment on a daily basis. Users will re-post some posts if they wish. Reposting has a significant effect on the transfer of information between users. Due to the large number of posts, users in these communities face the information overload problem. In this paper, the repost behavior of users in online communities is modeled. Firstly, effective factors have been identified in the behavior of user reposting, and then, using a reinforcement learning approach, users' repost behavior is anticipated. This reinforcement learning method is designed as a game for a team of random learning automata as a common pay-off game. To evaluate the proposed method, three large data sets have been gathered. Various scenarios have been used to evaluate the proposed method. Based on the results, randomized learning automata have great performance due to the features of the environment and online learning power.

کلیدواژه‌ها [English]

  • Online Community
  • Team of Learning Automata
  • Users behavior Modelingو Common Pay-off Game
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