Computer Science ›› 2016, Vol. 43 ›› Issue (7): 259-264.doi: 10.11896/j.issn.1002-137X.2016.07.047

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Weighted Bipartite Network Recommendation Algorithm Based on Increasing Similarity Coefficient

LI Zhen-dong, LUO Qi and SHI Li-li   

  • Online:2018-12-01 Published:2018-12-01

Abstract: The recommendation algorithm based on bipartite networks is a research hotspot in the personalized recommendation system,while the difficulty of research is how to make use of the users’ evaluation resources scientifically to work out an efficient and accurate recommendation for target users in the absence of rating data.Meanwhile,it has received sufficient attention of scholars.Therefore,a new recommendation algorithm was put forward with the monotonous saturation function as weight,and tangent of target users and other projects’ common rating numbers against the mean value of total users is used as traditional similarity coefficient.At the same time,after the coefficient gets adjusted,the similarity will be sorted in descending order,the set of the first K nearest neighbors of which can be utilized for target users’ recommendation.The experimental results prove that the revised algorithm improves the accuracy of re-commendation and reduces its complexity.

Key words: Personalized recommendation,Weighted bipartite networks,Monotonic saturation,Accuracy

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