Abstract
Modern recommender systems target the satisfaction of the end user through the personalization techniques that collects the history of the user's navigation. But the sole dependency on the user profile by means of navigation history alone cannot promise the quality of recommendations because of the lack of semantics. Though the literature provides many techniques to conceptualize the process they lead to high computational complexity due to considering the content data as input information. In this paper a hybrid recommender framework is developed that considers Meta data based conceptual semantics and the temporal patterns on top of the usage history. This framework also includes an online process that identifies the conceptual drift of the usage dynamically. The experimental results shown the effectiveness of the proposed framework when compared to the existing modern recommenders also indicate that the proposed model can resolve a cold start problem yet accurate suggestions reducing computational complexity.
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