A New Trust-Based Collaborative Filtering Measure Using Bhattacharyya Coefficient

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1042)


With the rapid growth of network data and demands of users, the concept of AI in recommendation system has become a hot academic topic. However, in sparse data, it is difficult for the current user to obtain his efficient neighbors and for some cold-start users, it doesn’t do anything. Therefore, we constructed a new measure of trust between users for neighborhood based on Collaborative filtering(CF) which uses a pair of users common ratings and exploits Bhattacharyya similarity to finds relevance of each pair of rated items. We also have measured the validity of the proposed model through accuracy, recall rate and F1 measures. The results show that although some recall rates will be lost, the precision is greatly improved. Overall, it achieved good results.


Collaborative filtering Trust method Bhattacharyya coefficient Sparsity problem 



The paper is funded by the National Natural Science Foundation of Grant No. 61272036. Meanwhile, it is also funded by the Central University Fundamental Research Fund and the Key Discipline of Shanghai Second Polytechnic University. The grant numbers are NZ2013306 and XXKZD1604 respectively.


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.School of Computer and Information EngineeringShanghai Polytechnic UniversityShanghaiChina

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