پیش‌بینی امتیازات کاربران در سیستم‌های پیشنهاددهنده با درنظرگرفتن پویایی علایق کاربران و تغییرات ویژگی‌های اقلام
محورهای موضوعی : مهندسی برق و کامپیوتر
1 - گروه مهندسي كامپيوتر ، دانشگاه آزاد اسلامی واحد كاشمر، ایران
کلید واژه: سیستم‌های پیشنهاددهنده, پویایی علایق, پویایی اقلام, پیش‌بینی امتیازات, تجزیه نامنفی ماتریس.,
چکیده مقاله :
سیستم‌های پیشنهاددهنده برای استخراج اطلاعات مفید از حجم انبوهی از دادههای پیچیده به کاربران کمک کرده و استفاده از آنها در سال‌های اخیر مورد توجه‌ی چشم‌گیری قرار گرفته است. در عمل معمولا ًعلایق کاربران و ویژگی‌های اقلام در این سیستم‌ها در طول زمان تغییر می‌کنند و بنابراین تطبیق سیستم‌های پیشنهاددهنده با این نوع تغییرات ضروری بوده و به ارائه‌ی پیشنهاداتی دقیق‌تر به کاربران کمک می‌کند. با این وجود، اغلب سیستم‌های پیشنهاددهنده‌ی پویا، فقط مبتنی بر پویایی علایق کاربران در طول زمان هستند و تغییرات ویژگی‌های اقلام را در نظر نمی‌گیرند. در این مقاله، مدلی مبتنی بر تجزیه‌ی نامنفی ماتریس برای پیش‌بینی امتیازات کاربران به اقلام در سیستم‌های پیشنهاددهنده ارائه می‌شود که از هر دوی پویایی علایق کاربران و تغییرات ویژگی‌های اقلام در طول زمان استفاده می‌کند. در مدل پیشنهادی به منظور کاهش مشکل خلوتی داده‌ها، علاوه بر امتیازات کاربران از اطلاعات مربوط به اعتماد بین کاربران نیز استفاده می‌شود. نتایج ارزیابی بر روی مجموعه داده‌ی Epinions نشان می‌دهد که مدل پیشنهادی نسبت به روش‌های مورد مقایسه از دقت بهتری برخوردار می‌باشد.
Recommender systems help users to extract useful information from a large volume of complex data, and their use has received significant attention in recent years. In practice, the interests of users and the characteristics of items in these systems change over time, and therefore, adapting recommender systems to these types of changes is necessary and helps to provide more accurate recommendations to users. However, most temporal recommender systems are only based on the dynamics of users' preferences over time and do not consider changes in item characteristics.
In this paper, we propose a non-negative matrix factorization-based recommender system that uses both dynamics of users' interests and the changes in item characteristics over time in predicting users' ratings of items. In the proposed model, in order to reduce the data sparsity problem, in addition to users' ratings, trust between users is also used. The evaluation results on the Epinions dataset show that the proposed model is more accurate than the compared methods.
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