An Effective Hybrid Recommender Using Metadata-based Conceptualization and Temporal Semantics

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 user profile based on navigation alone cannot promise the quality of recommendations because of the lack of semantics of various aspect such as demographics of the user, time of usage, concept of need etc in the processing. Though the literature provides many techniques to conceptualize the process makes high computational complexity because of the content data considered 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 history of the usage. 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.