دوره 18، شماره 1 - ( 3-1400 )                   جلد 18 شماره 1 صفحات 28-13 | برگشت به فهرست نسخه ها

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Tahmasbi H, Jalali M, Shakeri H. A social recommender system based on matrix factorization considering dynamics of user preferences. JSDP. 2021; 18 (1) :28-13
URL: http://jsdp.rcisp.ac.ir/article-1-929-fa.html
طهماسبی حمیدرضا، جلالی مهرداد، شاکری حسن. یک سامانه پیشنهاددهنده اجتماعی مبتنی بر تجزیه ماتریس با در‌نظر‌گرفتن پویایی علایق کاربران. پردازش علائم و داده‌ها. 1400; 18 (1) :28-13

URL: http://jsdp.rcisp.ac.ir/article-1-929-fa.html


دانشگاه آزاد اسلامی واحد مشهد
چکیده:   (173 مشاهده)
در سال‌های اخیر، استفاده از سامانه‌‌های پیشنهاددهنده در شبکه‌های اجتماعی رشد قابل توجهی داشته است. در این سامانه‌‌ها، رفتار و علایق کاربران در طول زمان تغییر می‌کند و تطبیق سامانه‌های پیشنهاددهنده با این پویایی علایق و نیازهای کاربران به‌منظور ارائه پیشنهادات دقیق‌تر به کاربران ضروری است. علی‌رغم اهمیت این موضوع، اغلب سامانه‌‌های پیشنهاددهنده، رفتار پویای کاربر را در نظر نمی‌گیرند. در این مقاله، یک سامانه پیشنهاددهنده اجتماعی با در‌نظر‌گرفتن پویایی علایق کاربران ارائه می‌شود که از روش تجزیه ماتریس استفاده می‌کند. در مدل پیشنهادی با در‌نظر‌گرفتن این‌که هر کاربر الگوی تغییر علایق خاص خود را دارد، فرض می‌شود که علایق فعلی کاربر به علایق او در دوره زمانی قبلی بستگی دارد، و یک ماتریس انتقال علایق برای هر کاربر به‌منظور مدل‌کردن پویایی علایق کاربر بین دو دوره متوالی آموزش داده می‌شود و با ترکیب امتیازاتِ کاربران و اعتماد بین آن‌ها بر اساس روش تجزیه ماتریس، امتیازاتِ کاربران به اقلام پیش‌بینی می‌شود. ارزیابی‌ها بر روی مجموعه داده Epinions نشان می‌دهند که مدل پیشنهادی نسبت به روش‌های مقایسه‌شده، منجر به بهبود بیشتر دقت در پیش‌بینی امتیازات می‌‌شود. همچنین تحلیل پیچیدگی زمانی مدل پیشنهادی بیان‌گر مقیاس‌پذیر‌بودن این مدل  است.
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نوع مطالعه: پژوهشي | موضوع مقاله: مقالات پردازش داده‌های رقمی
دریافت: 1397/8/22 | پذیرش: 1399/5/28 | انتشار: 1400/3/1 | انتشار الکترونیک: 1400/3/1

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