سال انتشار: ۱۳۸۵

محل انتشار: دوازدهمین کنفرانس سالانه انجمن کامپیوتر ایران

تعداد صفحات: ۴

نویسنده(ها):

Nima Taghipour – Department of Computer Egineering , Faculty of Computer Engineering & Information Technology Amirkabir Universoty of Technology, Tehran, Iran
Saeed Ghidary – Department of Computer Egineering , Faculty of Computer Engineering & Information Technology Amirkabir Universoty of Technology, Tehran, Iran
Ahmad Kardan – Department of Computer Egineering , Faculty of Computer Engineering & Information Technology Amirkabir Universoty of Technology, Tehran, Iran

چکیده:

With the rapid growth of the World Wide Web, the amount of information available online is increasing with an enormous pace. Recommender systems aim at pruning this information space and directing users toward the items that best meet their needs and interests. In this paper we propose a novel machine learning perspective toward the problem, based on reinforcement learning. We model the problem as Q-Learning ,employing concepts and techniques commonly appliedin the web usage mining domain. We propose that reinforcement learning paradigm provides an appropriate model for the recommendation problem, as well as a framework in whichthe system constantly interacts with the user and learns from her behavior. Our experimental evaluations supports our claims and demonstrate how this approach can improve the quality of web recommendations.