Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
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
Communications of the ACM
MovieLens unplugged: experiences with an occasionally connected recommender system
Proceedings of the 8th international conference on Intelligent user interfaces
A Case-Based Reasoning View of Automated Collaborative Filtering
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Category-Based Filtering in Recommender Systems for Improved Performance in Dynamic Domains
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Recommender systems: a market-based design
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Web usage mining: discovery and application of interesting patterns from web data
Web usage mining: discovery and application of interesting patterns from web data
Developing recommender systems with the consideration of product profitability for sellers
Information Sciences: an International Journal
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Semantic Web Recommender Systems is more complex than traditional Recommender System in that it raises many new issues such as user profiling, navigation pattern. Semantic Web based Recommender Service aims at combining the two fast-developing research areas Semantic Web and User XQuery. Nevertheless, as the number of web pages increases rapidity, the problem of the information overload becomes increasingly severe when browsing and searching the World Wide Web. To solve this problem, personalization becomes a popular solution to customize the World Wide Web environment towards a user’s preference. The idea is to improve by analyze of user query pattern for recommender service in the Web and to make use for building up the Semantic Web. In this paper, we present a user XQuery method for personalization Service using Semantic Web.