Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Automatic text processing
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Pattern Recognition Letters
The World-Wide Web: quagmire or gold mine?
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Adaptive Web sites: automatically synthesizing Web pages
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
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Artificial Intelligence - Special issue on Intelligent internet systems
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Information Retrieval
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EC-WEB '00 Proceedings of the First International Conference on Electronic Commerce and Web Technologies
Towards Zero-Input Personalization: Referrer-Based Page Prediction
AH '00 Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
SEWeP: using site semantics and a taxonomy to enhance the Web personalization process
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A graph model for E-commerce recommender systems
Journal of the American Society for Information Science and Technology
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
Taxonomy-driven computation of product recommendations
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Web personalization integrating content semantics and navigational patterns
Proceedings of the 6th annual ACM international workshop on Web information and data management
A Knowledge Base for the maintenance of knowledge extracted from web data
Knowledge-Based Systems
An Improved Genetic k-means Algorithm for Optimal Clustering
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Website browsing aid: A navigation graph-based recommendation system
Decision Support Systems
Web Page Personalization Based on Weighted Association Rules
ICECT '09 Proceedings of the 2009 International Conference on Electronic Computer Technology
Adaptive Web Sites: A Knowledge Extraction from Web Data Approach - Volume 170 Frontiers in Artificial Intelligence and Applications
Expert Systems with Applications: An International Journal
A fuzzy bi-clustering approach to correlate web users and pages
International Journal of Knowledge and Web Intelligence
A Hybrid Web Recommender System Based on Cellular Learning Automata
GRC '10 Proceedings of the 2010 IEEE International Conference on Granular Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
International Journal of Hybrid Intelligent Systems
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Many efforts have been done to tackle the problem of information abundance in the World Wide Web. Growth in the number of web users and the necessity of making the information available on the web, make web recommender systems very critical and popular. Recommender systems use the knowledge obtained through the analysis of users' navigational behavior, to customize a web site to the needs of each particular user or set of users. Most of the existing recommender systems use either content-based or collaborative filtering approach. It is difficult to decide which one of these approaches is the most effective one to be used, as each of them has both strengths and weaknesses. Therefore, a combination of these methods as a hybrid system can overcome the limitations and increase the effectiveness of the system. This paper introduces a new hybrid recommender system by exploiting a combination of collaborative filtering and content-based approaches in a way that resolves the drawbacks of each approach and makes a great improvement over a variety of recommendations in comparison to each individual approach. We introduce a new fuzzy clustering approach based on genetic algorithm and create a two-layer graph. After applying this clustering algorithm to both layers of the graph, we compute the similarity between web pages and users, and propose recommendations using the content-based, collaborative and hybrid approaches. A detailed comparison on all the mentioned approaches shows that the hybrid approach recommends the web pages which haven't been yet viewed by any user, more accurately and precisely than other approaches. Therefore, the evaluation of the results reveals that the novel proposed hybrid approach achieves more accurate predictions and more appropriate recommendations than each individual approach.