An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
ACM Transactions on Information Systems (TOIS)
Information Diffusion Approach to Cold-Start Problem
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
A Content Recommendation System Based on Category Correlations
ICCGI '10 Proceedings of the 2010 Fifth International Multi-conference on Computing in the Global Information Technology
Analyzing category correlations for recommendation system
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
A movie recommendation algorithm based on genre correlations
Expert Systems with Applications: An International Journal
An effective recommendation algorithm for improving prediction quality
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Representative reviewers for Internet social media
Expert Systems with Applications: An International Journal
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With the development of the Internet, the users share information using Web applications. Because of this reason, there is lots of information on the Web. The information includes not only high quality information, but also useless one. With the phenomena, the recommendation system appears on the Web. Existing information recommendation systems on the Web have known problems. One famous problem is cold-start. We tackle the cold-start problem for a new item in recommendation system. To alleviate cold-start for a new item, we use method for identifying representative reviewers in raters group and recommendation algorithm based on category correlations. The representative reviewers mean the users who represent their raters group. Namely, the ratings of the reviewers can represent the average ratings of other users. If there are the ratings for new items rated by the representative reviewers, then we can consider the ratings rated by many other users. We predict the ratings of these reviewers for a new item. To predict ratings, we use the recommendation algorithm based on the category correlations. This algorithm can draw the prediction results without ratings since the algorithm uses category information. We propose the prediction results of the representative reviewers as the representative ratings for a new item. We propose the algorithm to alleviate cold-start for a new item and show the reliability of our approach through tests.