Document Type : scientific-research
Authors
1 Department of Computer Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
2 Department of Computer Engineering, University of Kurdistan
Abstract
Massive open online courses (MOOCs) have recently becoming a popular means of education. They generally give the students large-scale options. However, the diversity of MOOC courses available and their rapid updates make it more difficult for students to find fresh material relevant to them. A recommendation system (RS) connects the learner with the best learning resources to meet students' interests. The majority of recommender system research is based on the existence of explicit feedback, which is often impossible or inaccessible in MOOCs. As a result, in this paper, we model user positive and negative preferences using implicit feedback acquired passively by watching various types of students' behavior. This paper proposes a novel course recommendation, which employs Siamese Neural Networks (SNNs) to extract latent representations of students and courses using a loss function that favors observed over unobserved courses. The similarity of users and courses is then determined using a novel representation mechansim. Furthermore, recommending those courses with limited interaction data is a major challenge in MOOC recommenders. To tackle this issue, the courses profiles are used as side information which helps us create more accurate representations. To evaluate the performance of the propsoed method, we performed the experiments on a real dataset gathered from XuetangX—one of China's largest MOOCs. The results of the experiments show that the proposed method outperforms a number of baseline nad state-of-the-art MOOC recommenders.
Keywords
Article Title [Persian]
یک سیستم توصیه بازخورد ضمنی برای دوره های آنلاین آزاد گسترده
Authors [Persian]
- آزاده فاروقی 1
- پرهام مرادی 2
1 گروه مهندسی کامپیوتر دانشگاه کردستان
2 گروه مهندسی کامپیوتر دانشگاه کردستان
Abstract [Persian]
دوره های گسترده آنلاین باز (MOOCs) اخیراً به یک ابزار آموزشی محبوب تبدیل شده اند. آنها به طور کلی گزینه های در مقیاس بزرگ را به دانش آموزان می دهند. با این حال، تنوع دوره های MOOC موجود و به روز رسانی سریع آنها، یافتن مطالب جدید مرتبط با آنها را برای دانش آموزان دشوارتر می کند. یک سیستم توصیه (RS) یادگیرنده را با بهترین منابع یادگیری برای برآورده کردن علایق دانش آموزان مرتبط می کند. اکثر تحقیقات سیستم توصیه‌گر بر اساس وجود بازخورد صریح است که اغلب در MOOC غیرممکن یا غیرقابل دسترسی است. در نتیجه، در این مقاله، ترجیحات مثبت و منفی کاربر را با استفاده از بازخورد ضمنی که به طور منفعلانه با مشاهده انواع مختلف رفتار دانش‌آموزان به دست می‌آید مدل می‌کنیم. این مقاله یک توصیه دوره جدید را پیشنهاد می‌کند، که از شبکه‌های عصبی سیامی (SNN) برای استخراج بازنمایی‌های نهفته از دانش‌آموزان و دوره‌ها با استفاده از یک تابع ضرر استفاده می‌کند که به نفع مشاهده نسبت به دوره‌های مشاهده نشده است. سپس شباهت کاربران و دوره ها با استفاده از یک مکانیزم نمایش جدید تعیین می شود. علاوه بر این، توصیه دوره‌هایی با داده‌های تعامل محدود یک چالش بزرگ در توصیه‌کنندگان MOOC است. برای مقابله با این موضوع، از نمایه دوره ها به عنوان اطلاعات جانبی استفاده می شود که به ما کمک می کند تا نمایش های دقیق تری ایجاد کنیم. برای ارزیابی عملکرد روش پیشنهادی، آزمایش‌ها را روی یک مجموعه داده واقعی جمع‌آوری‌شده از XuetangX انجام دادیم که یکی از بزرگترین MOOCهای چین است. نتایج آزمایش‌ها نشان می‌دهد که روش پیشنهادی از تعدادی از توصیه‌کنندگان MOOC مبنا و پیشرفته‌تر عمل می‌کند.
Keywords [Persian]
- MOOCs بازخورد ضمنی
- سیستم توصیه شبکه عصبی سیامی
- اطلاعات محتوا
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