From user access patterns to dynamic hypertext linking
Proceedings of the fifth international World Wide Web conference on Computer networks and ISDN systems
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Automatic personalization based on Web usage mining
Communications of the ACM
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
Selective Markov models for predicting Web page accesses
ACM Transactions on Internet Technology (TOIT)
An Online Recommender System for Large Web Sites
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Summarizing local context to personalize global web search
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Dynamic personalization of web sites without user intervention
Communications of the ACM - Spam and the ongoing battle for the inbox
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Evaluating Performance of Recommender Systems: An Experimental Comparison
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Expert Systems with Applications: An International Journal
Bee-Inspired Protocol Engineering: From Nature to Networks
Bee-Inspired Protocol Engineering: From Nature to Networks
A survey: algorithms simulating bee swarm intelligence
Artificial Intelligence Review
Sparse LCS common substring alignment
CPM'03 Proceedings of the 14th annual conference on Combinatorial pattern matching
Term-frequency Based Feature Selection Methods for Text Categorization
ICGEC '10 Proceedings of the 2010 Fourth International Conference on Genetic and Evolutionary Computing
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Online recommendation system is the modern software system used in all the e-commerce sites to capture the user intent and recommend the web pages that contain user expected information. The important challenges for such a system must include a need of being self-adaptive because the needs for online users may change dynamically. Classifier plays a very important role to improve the overall system accuracy. Here, we proposed the Ontology driven bee's foraging approach (ODBFA) that accurately classify the current user activity to any of the navigation profiles and predict the navigations that most likely to be visited by online users. Our proposed ODBFA method uses the Honey bee foraging behaviour in selecting the more profitable navigation profile for the current user activity. This approach makes the system self adaptive by capturing the changing needs of online user with the help of ontological framework comprising of ontology based similarity comparison and scoring algorithm. This approach effectively outperforms the other methods in achieving accurate classification and prediction of future navigation for the current online user.