بهبود پیشنهادهای سرویس در اینترنت اشیاء اجتماعی: یک رویکرد پالایش همخوان تطبیقی با استفاده از شباهت مبتنی بر دوستی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی کامپیوتر- نرم افزار و الگوریتم ، دانشگاه یزد، یزد، ایران

2 استادیار، دانشکده مهندسی، دانشگاه یزد، یزد، ایران

10.22091/jemsc.2025.11431.1210

چکیده

اینترنت اشیاء اجتماعی (SIoT) با ترکیب تعاملات اجتماعی و فناوری اینترنت اشیاء، محیط‌های هوشمند و یکپارچه‌ای ایجاد می‌کند که در آن، اشخاص از سرویس‌های ارائه‌شده توسط اشیاء تحت مالکیت دیگران بهره‌مند می‌شوند. در این شبکه، افراد با ایجاد روابط دوستی می‌توانند از خدماتی استفاده کنند که شخصی‌سازی‌شده و متناسب با علایق آن‌ها باشد. با افزایش مقیاس شبکه و تعداد اشخاص، اهمیت ارائه سرویس‌های متناسب بیشتر می‌شود. این مقاله به تحلیل مجموعه داده‌های واقعی شهر سانتاندر از نظر تعداد اشخاص، توزیع دوستی‌ها و نوع توزیع آن‌ها پرداخته و یک الگوریتم پالایشگر همخوان تطبیقی مبتنی بر جوامع دوستی (A-CFA-FC) را معرفی می‌کند که با در نظر گرفتن روابط دوستی و ترجیحات، جوامع دوستی را تشخیص داده و سرویس‌های متناسب پیشنهاد می‌دهد. نتایج اجرای الگوریتم روی داده‌های شهر سانتاندر نشان می‌دهد که این الگوریتم با شاخص شباهت جاکارد و پیچیدگی زمانی کمتر، تعداد بیشتری جوامع دوستی را تشخیص می‌دهد و در مقایسه با الگوریتم‌های پایه، میانگین مربعات ریشه خطا را حدود 17 درصد و امتیاز F1 را حدود 21 درصد بهبود می‌بخشد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Enhancing Service Recommendations in the Social Internet of Things: An Adaptive Collaborative Filtering Approach Using Friendship-Based Similarity

نویسندگان [English]

  • Mohammad Mahdian 1
  • S. Mojtaba Matinkhah 2
1 PhD Student. Department of Computer Engineering, Yazd University, Yazd, Iran.
2 Assistant prof. Department of Computer Engineering, Yazd University, Yazd, Iran
چکیده [English]

The Social Internet of Things (SIoT) integrates social interactions with IoT technology to create intelligent, connected environments. In this network, people form friendships, and objects owned by them provide services to others. As the SIoT network grows, offering personalized services tailored to individual interests becomes increasingly important. This paper examines real-world data from the city of Santander, analyzing the number of users, friendship degree distribution, and its patterns. An adaptive consonance filter algorithm based on friendship communities (A-CFA-FC) is proposed, which uses friendship relations and individual preferences to identify friendship communities based on a similarity index. The algorithm ranks and recommends services according to user interests within the SIoT environment. Results from the Santander dataset show that the proposed algorithm, using the Jaccard similarity index, detects more communities with lower time complexity and higher compactness. Compared to the baseline algorithm, it reduces root mean square error by about 17% and improves the F1 score by approximately 21%.

کلیدواژه‌ها [English]

  • Adaptive Filtering Algorithm
  • User Preference Matching
  • Community Detection
  • Service Personalization

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