An efficient recommendation generation using relevant Jaccard similarity

In the literature, various collaborative filtering approaches have been developed to perform an efficient recommendation on top of reducing the search cost of the customers. The recommender system methods are concentrated on improving the accuracy and to achieve that goal they focused on formulating complex similarity approaches and neglect the computation time in their model. Furthermore, in order to compute the similarity metric, most of traditional similarity measures have only considered co-rated items and overlooked the total rating vector of the user or item. However, considering only co-rated items to measure similarity metrics in recommender system is an insignificant approach to identifying appropriate nearest neighbors in relatively sparse datasets. Therefore, in this research, two new simple but effective similarity models have been developed by considering all rating vectors of users to classify relevant neighborhoods and generate recommendations in a lower computation time. Moreover, MovieLens, a well-known dataset used in recommender system domain, is involved here to validate the performance of the proposed model. It seems that the proposed relevant Jaccard similarity perform more accurately and effectively to generate well recommendation than other traditional similarity models. (C) 2019 Elsevier Inc. All rights reserved.