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Recommender system with grey wolf optimizer and FCM

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Abstract

Recommender systems are contributing a significant aspect in information filtering and knowledge management systems. They provide explicit and reliable recommendations to the users so that user can get information about all products in e-commerce domain. In the era of big data and large complex information delivery system, it is impossible to get the right information in the online environment. In this research work, we offered a novel movie-based collaborative recommender system which utilizes the bio-inspired gray wolf optimizer algorithm and fuzzy c-mean (FCM) clustering technique and predicts rating of a movie for a particular user based on his historical data and similarity of users. Gray wolf optimizer algorithm was applied on the Movielens dataset to obtain the initial clusters, and also the initial positions of clusters are obtained. FCM is used to classify the users in the dataset by similarity of user ratings. Our proposed collaborative recommender system performed extremely well with respect to accuracy and precision. We analyzed our proposed recommender system over Movielens dataset which is available publically. Various evaluation metrics were utilized such as mean absolute error, standard deviation, precision and recall. We also compared the performance of projected system with already established systems. The experiment results delivered by proposed recommender system demonstrated that efficiency and performance are enhanced and also offered better recommendations when compared with our previous work [1].

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Delhi Technological University, Delhi, India

    Rahul Katarya & Om Prakash Verma

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  1. Rahul Katarya
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  2. Om Prakash Verma
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Correspondence to Rahul Katarya.

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Katarya, R., Verma, O.P. Recommender system with grey wolf optimizer and FCM. Neural Comput & Applic 30, 1679–1687 (2018). https://doi.org/10.1007/s00521-016-2817-3

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  • DOI: https://doi.org/10.1007/s00521-016-2817-3

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