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DOI: 10.14569/IJACSA.2016.070408
PDF

Incorporating Multiple Attributes in Social Networks to Enhance the Collaborative Filtering Recommendation Algorithm

Author 1: Jian Yi
Author 2: Xiao Yunpeng
Author 3: Liu Yanbing

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 4, 2016.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: In view of the existing user similarity calculation principle of recommendation algorithm is single, and recommender system accuracy is not well, we propose a novel social multi-attribute collaborative filtering algorithm (SoMu). We first define the user attraction similarity by users’ historical rated behaviors using graph theory, and secondly, define the user interaction similarity by users’ social friendship which is based on the social relationship of being followed and following. Then, we combine the user attraction similarity and the user interaction similarity to obtain a multi-attribute comprehensive user similarity model. Finally, realize personalized recommendation according to the comprehensive similarity model. Experimental results on Douban and MovieLens show that the proposed algorithm successfully incorporates multiple attributes in social networks to recommendation algorithm, and improves the accuracy of recommender system with the improved comprehensive similarity computing model.

Keywords: Recommender System; Social Networks; Collaborative Filtering; Comprehensive Similarity

Jian Yi, Xiao Yunpeng and Liu Yanbing, “Incorporating Multiple Attributes in Social Networks to Enhance the Collaborative Filtering Recommendation Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 7(4), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070408

@article{Yi2016,
title = {Incorporating Multiple Attributes in Social Networks to Enhance the Collaborative Filtering Recommendation Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070408},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070408},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {4},
author = {Jian Yi and Xiao Yunpeng and Liu Yanbing}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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