Journal of Guangdong University of Technology ›› 2019, Vol. 36 ›› Issue (03): 39-46.doi: 10.12052/gdutxb.180112

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A Social Network Recommendation Algorithm Combining Strong and Weak Ties and Interests

He Wei-jun1, Zhou Ying-tang2   

  1. 1. School of Management, Guangdong University of Technology, Guangzhou 510520, China;
    2. College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
  • Received:2018-08-16 Online:2019-05-09 Published:2019-04-04

Abstract: Social network recommender systems combining strong and weak ties and interests are proposed. First, social relationship sets for customers are built based on the strong and weak ties, taking into account the breadth and depth of information transfer. Then, based on the association rules to improve the state transition probability of the PageRank Algorithm, the modified matrix is capable of measuring the social closeness between different customers more reasonably. Meanwhile, aiming to increase diversity and novelty of the systems, the similarity of interests between different customers is considered giving them the weight of voting in the candidate projects. Finally, the candidate items are scored and Top-N filtered to obtain a recommendation list. As is shown in the experiment results, the algorithm is more reasonable and effective than the reference algorithms.

Key words: recommender systems, PageRank algorithm, weak ties, Jaccard index

CLC Number: 

  • C931.6
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