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
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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
Lessons from the Netflix prize challenge
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
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
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
A movie recommendation algorithm based on genre correlations
Expert Systems with Applications: An International Journal
Identifying representative ratings for a new item in recommendation system
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
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Since the late 20th century, the Internet users have noticeably increased and these users have provided lots of information on the Web and searched for information from the Web. Now there are huge amount of new information on the Web everyday. However, not all data are reliable and valuable. This implies that it becomes more and more difficult to find a satisfactory result from the Web. We often iterate searching several times to find what we are looking for. Researcher suggests a recommendation system to solve this problem. Instead of searching several times, a recommendation system proposes relevant information. In the Web 2.0 era, a recommendation system often relies on the collaborative filtering from users. In general, the collaborative filtering approach works based on user information such as gender, location or preference. However, it may cause the cold-star problem or the sparsity problem since it requires initial user information. Recently, there are several attempts to tackle these collaborative filtering problems. One of such attempts is to use category correlation of contents. For instance, a movie has genre information given by movie experts and directors. We notice that these category information are more reliable compared with user ratings. Moreover, a newly created content always has category information; namely, we can avoid the cold-start problem. We consider a movie recommendation system. We revisit the previous algorithm using genre correlation and improve the algorithm. We also test the modified algorithm and analyze the results with respect to a characteristic of genre correlations.