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Guest Editorial—Recommender Systems for Web Intelligence

Yue Xu1, Yuefeng Li1, and Audun Jøsang2
1. Discipline of Computer Science, Faculty of Science and Technology, Queensland University of Technology, Australia
2. University of Oslo, Unik Graduate Center, Norway
Information overload has become a serious concern with the explosive growth of resources available through the Internet. Web users are commonly overwhelmed by huge amount of information and are faced with the challenge of finding the most relevant and reliable information in a timely manner. It is critical to use intelligent agent software systems to assist Web users in finding the right information from an abundance of Web data. Significant research has been undertaken to build support tools that ensure the right information is delivered to the right people when they access the Internet. Recommender systems are one way of helping users to deal with such information explosion by recommending items (e.g., information and products) that match users’ personal interests. Recommender systems represent tools for efficient selection of the most relevant and reliable resources, and the interest in such systems has increased dramatically over the last few years [1]. Since the appearance of the first paper [2] on collaborative filtering in recommender systems , Web based recommender systems have been developed in many application domains [3]–[5]. In this special issue, two papers present the application of recommender systems.

Index Terms—recommender system, personalization, trust, information overload

Cite: Yue Xu, Yuefeng Li, and Audun Jøsang, "Guest Editorial—Recommender Systems for Web Intelligence," Journal of Emerging Technologies in Web Intelligence, Vol. 2, No. 4, pp. 269-271, November 2010. doi:10.4304/jetwi.2.4.269-271
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