Introduction to artificial intelligence and expert systems
Introduction to artificial intelligence and expert systems
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Fab: content-based, collaborative recommendation
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Novelty and redundancy detection in adaptive filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
IEEE Transactions on Knowledge and Data Engineering
Digital Content Recommender on the Internet
IEEE Intelligent Systems
Collaborative Filtering Using Dual Information Sources
IEEE Intelligent Systems
Informed Recommender: Basing Recommendations on Consumer Product Reviews
IEEE Intelligent Systems
New Recommendation Techniques for Multicriteria Rating Systems
IEEE Intelligent Systems
MarCol: A Market-Based Recommender System
IEEE Intelligent Systems
Preference-based graphic models for collaborative filtering
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Personalized TV services based on TV-anytime for personal digital recorder
IEEE Transactions on Consumer Electronics
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The amount of information on the Web is increasing exponentially due to the rapid development of information communication technology. In the field of e-business in particular, research into recommendation systems that analyze user preferences and suggest related products has been an issue. Analyzing the preferences of users is very important for making recommendations. However, if there is a lack of user information when a predictive method is being applied, no suggestion can be made. Therefore, this paper proposes a personalized recommendation system that utilizes the information derived from analyzing the data regarding user behaviors. The proposed system observes the various actions of a user on the Web in order to accurately understand his or her intentions. In addition, the most preferred qualities of various products is inferred based on the ID3 algorithm and the Naive Bayesian algorithm is applied to analyze user preferences by giving different weight to different product attributes. In this way, the proposed system can provide accurate recommendations even in situations where data regarding the behavior of a user is lacking. In order to evaluate the efficiency of the proposed system, the recommendation results and the number of available behavior data were compared, thus confirming its effectiveness.