FNHSMHRS: Combined Recommender System Based On Fuzzy Clustering and Exploratory Similarity Criteria

Authors

20.1001.1.27174409.1398.2.2.3.3=DOR

Abstract

Today, Referral Systems Have Become A Pervasive System For Guiding Users In The Vast Amount Of Data On The Internet. Collaborative Refinement, Which Offers Suggestions To The Active User Based On The Scoring Of A Set Of Users, Is One Of The Simplest, Most Understandable And Successful Models For Finding Like-Minded People In Recommender Systems. In This Model, ‌ With Increasing Number Of Users And Items, The System Suffers From Scalability. On The Other Hand, Improving System Performance Is Important When We Have Little Information About The Benefits Available. In This Paper, A Hybrid Recommender System Called FNHSM_HRS Based On The Exploratory Similarity Criterion (NHSM) With Fuzzy Clustering ‌ Is Presented. The Use Of Fuzzy Clustering Method In The Proposed System Improves The Scalability Problem And Increases The Accuracy Of System Proposals. The Proposed System Is Based On The Collaborative Refinement Model And Improves The Performance And Accuracy Of The System By Using The Heuristic Similarity Criterion. Evaluation Of The Results Of The Proposed System Is Performed On The Movielens Dataset. The Evaluation Results Using MAE, Accuracy, Precision and Recall Criteria Show the Improvement of System Performance and Increase the Accuracy of the Proposals Compared To Collaborative Refinement Methods That Use Other Criteria to Find Similarity

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References

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History

  • Receive Date: 09 October 2019
  • Revise Date: 03 September 2020
  • Accept Date: 31 August 2020

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