Improving the Accuracy of Collaborative Filtering-Based Recommendations by Considering the Temporal Variance of Top-N Neighbors

  • Pradeep Kumar SinghEmail author
  • Showmik Setta
  • Pijush Kanti Dutta Pramanik
  • Prasenjit Choudhury
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1087)


The accuracy of the recommendation process based on neighborhood-based collaborative filtering tends to diverge because the interests/preferences of the neighbors are likely to change along with time. The traditional recommendation methods do not consider the shifted likings of the neighbors; hence, the calculated set of neighbors does not always reflect the optimal neighborhood at any given point of time. In this paper, we propose a novel approach to calculate the similarity between users and find the similar neighbors of the target user in different time period to improve the accuracy in personalized recommendation. The performance of the proposed algorithm is tested on the MovieLens dataset using different performance metrics viz. MAE, RMSE, precision, recall, F-score, and accuracy.


Recommender systems Collaborative filtering Similarity metrics Prediction approach Rating Top-n neighbor Time period Cluster MAE RMSE Precision Recall F-score Accuracy 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Pradeep Kumar Singh
    • 1
    Email author
  • Showmik Setta
    • 2
  • Pijush Kanti Dutta Pramanik
    • 1
  • Prasenjit Choudhury
    • 1
  1. 1.Department of Computer Science and EngineeringNational Institute of Technology DurgapurDurgapurIndia
  2. 2.Department of Computer ApplicationTechno India HooghlyChinsurahIndia

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