A Hybrid Content Recommender Systems Based On Q-Learning To Recognized Learners Preferences
Publish place: International Conference on E-learning
Publish Year: 1388
نوع سند: مقاله کنفرانسی
زبان: English
View: 2,513
This Paper With 6 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
ICELEARNING04_062
تاریخ نمایه سازی: 7 مرداد 1388
Abstract:
Recommender systems play an important role in learning process by predicting user preferences. Learning process needs dynamic interactions between the learner and the learning system to recognize learner abilities, behaviors or other learner characteristics. Recommender systems have become increasingly popular in entertainment and e-commerce domains, but they have a little success in the elearning domains. Recommender systems learn about user preference over time, automatically finding things of similar interest. It reduces the burden of creating explicit queries during the learning process. Recommender systems use some techniques to recognize learners' preferences, such as filtering, machine learning techniques or hybrid techniques. In e-learning, some of these techniques can cause some problems or may be impossible to implement. This paper investigates a technique for recommender systems suitable for the learning environments to recognizing learners' preferences in the learning process. This technique predicts user preferences in order to identify a useful set of items and to be recommended in response to the learners specific information need. We propose a hybrid technique based on machine learning to recognize learner preferences and predict theirs required contents with high accuracy.
Keywords:
Authors
Ahmad A. Kardan
faculty member of Department of computer Engineering and information technology, AmirKabir University of Technology, Tehran, Iran
Omid R. B Speily
Advanced E Learning Technologies Lab, AmirKabir University of Technology
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :