Ontology driven bee's foraging approach based self adaptive online recommendation system

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.

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