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یک مدل عصبی خودتوجه آگاه به موقعیت برای توصیه مبتنی بر جلسه شخصی سازی شده | ||
مجله مهندسی برق دانشگاه تبریز | ||
دوره 55، شماره 1 - شماره پیاپی 111، خرداد 1404، صفحه 165-176 اصل مقاله (712.18 K) | ||
نوع مقاله: علمی-پژوهشی | ||
شناسه دیجیتال (DOI): 10.22034/tjee.2024.60844.4821 | ||
نویسندگان | ||
Azam Ramazani؛ Ali-Mohammad Zareh-Bidoki* ؛ Mohammad-Reza Pajoohan | ||
Department of Computer Engineering, Yazd University, Yazd, Iran | ||
چکیده | ||
سیستمهای توصیه مبتنیبرجلسه شخصیسازیشده، کلیک یا تعامل بعدی کاربر را بر اساس تعاملات قبلی کاربر در جلسه فعلی و جلسات تاریخی پیشبینی میکنند. مطالعات اخیر، بر روی شبکههای خودتوجه (SAN) برای بدست آوردن علایق کلی کاربران متمرکز شدهاند. شبکههای خودتوجه با مدل کردن وابستگیهای کلی بین تعاملات جلسه، توانایی بالایی در توصیه مبتنیبرجلسه، در مقایسه با دیگر رویکردهای شبکههای عمیق از خود نشان دادهاند. اما این شبکهها موقعیت و ترتیب اقلام در جلسه را در نظر نمیگیرند. درحالیکه اطلاعات متوالی اقلام جلسه میتواند علایق ترتیبی کاربران را منعکس کند. در این مقاله، یک مدل عصبی خودتوجه آگاهبهموقعیت (PASAN) برای توصیه مبتنی-برجلسه شخصیسازیشده پیشنهاد میشود. این رویکرد، به منظور درنظرگرفتن ترتیب توالی جلسات، از یک مکانیزم رمزگذاری موقعیت معکوس برای اختصاص دادن یک تعبیه موقعیت به اقلام، مبتنی بر ترتیب آنها در جلسه استفاده میکند. PASAN به طور مشترک از طریق شبکه خودتوجه، علایق کلی و از طریق رمزگذاری موقعیتی، علایق ترتیبی را یاد میگیرد. علاوه بر این، PASAN پیشنهادی علاوه بر جلسه فعلی از جلسات تاریخی کاربر هم استفاده و ترجیحات بلندمدت کاربران را مدل میکند. ابتدا PASAN مبتنی بر جلسات ناشناس آموزش داده میشود و سپس برای هر کاربر از طریق ترکیب وزنی جلسه فعلی و جلسات تاریخی کاربر، توصیههای شخصیسازیشده فراهم میشود. آزمایشهای انجام شده بر روی دو مجموعه داده واقعی نشان میدهد مدل پیشنهادی در مقایسه با سایر روشها بهتر عمل میکند. مدل پیشنهادی بر روی مجموعه داده Reddit، از نظر دقت حدود 20 درصد و از نظر میانگین رتبه متقابل حدود 8 درصد بهبود یافته است. | ||
کلیدواژهها | ||
توصیه مبتنی بر جلسه؛ توصیه شخصی سازی شده؛ شبکه های خودتوجه؛ یادگیری عمیق | ||
مراجع | ||
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آمار تعداد مشاهده مقاله: 243 تعداد دریافت فایل اصل مقاله: 53 |