مدل توصیه مکانهای مورد علاقه با توجه به الگوی رفتاری افراد بر اساس لیست دوستان بر پایه یادگیری عمیق
الموضوعات :
1 - دانشگاه آزاد اسلامی واحد مشهد
2 - دانشگاه آزاد اسلامی واحد مشهد
الکلمات المفتاحية: خوشهبندی انتقال میانگین, شبکه عصبی کانولوشن, شبکههای اجتماعی, نقاط مورد علاقه (POI),
ملخص المقالة :
رشد سریع شبکههای اجتماعی مبتنی بر مکان، فرصتی عالی برای ارائه خدمات توصیه مکانهای مورد علاقه به صورت هدفمند میباشد. یک وظیفه مهم برای توصیه دقیق نقاط جذاب و مورد علاقه کاربران در شبکههای اجتماعی مبتنی بر مکان، با توجه به چالشهای متون غنی و پراکندگی دادهها، بررسی ویژگیهای معنادار کاربران و نقاط مورد علاقه است. در این مقاله، یک روش جدید برای توصیه ترتیب دقیق بهترین نقاط مورد علاقه کاربران ارائه شده که ترکیبی از رویکردهای شبکه عصبی کانولوشن، خوشهبندی و دوستی میباشد. برای یافتن شباهت در رفتار دوستان صمیمی، از روش خوشهبندی انتقال میانگین استفاده میکنیم و فقط تأثیر الگوی رفتاری شبیهترین دوست را به نسبت همه دوستان کاربر در نظر میگیریم. چارچوب جدید شبکه عصبی کانولوشن پیشنهادی با ۱۰ لایه میتواند طول و عرض جغرافیایی و شناسه مکانهای مناسب بعدی را پیشبینی کرده و سپس بر اساس کوتاهترین فاصله از الگوی رفتاری دوست مشابه، مکانهای پیشنهادی را انتخاب کند. این رویکرد ترکیبی، در دو مجموعه داده شبکههای اجتماعی مبتنی بر مکان ارزیابی شده و نتایج تجربی نشان میدهد که استراتژی ما از روشهای پیشرفته توصیه نقاط مورد علاقه دقیقتر عمل میکند.
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