Improving the accuracy of item recommendations by combining collaborative and content-based recommendations: a hybrid approach
by Desabandhu Parasuraman; Sathiyamoorthy Elumalai
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 19, No. 3/4, 2021

Abstract: Recommender systems facilitate the users by providing the ample information of the items or the products they are interested. Users would not be aware of item details without the help of recommender systems due to the size of information available on the web. Collaborative filtering and content-based filtering are the two traditional filtering techniques of recommender systems. Both the filtering techniques have their advantages and certainly the disadvantages too. This can be solved by combining both the filtering techniques and improves the accuracy of recommendations. This leads to system as a hybrid recommender system. This paper presents a novel hybrid approach by combining a dynamic item-based collaborative filtering with the content-based filtering. Time variance and machine learning algorithms are applied on the filtering techniques to overcome the problems in recommendations. The approach is demonstrated using the MovieLens data sets to ensure the effectiveness of the proposed hybrid system.

Online publication date: Wed, 21-Jul-2021

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