An improved collaborative filtering recommendation algorithm

doi:10.3969/j.issn.1000-1565.2020.01.012

Abstract

Abstract: The collaborative filtering recommendation algorithm is one of the most widely used algorithms in the personalized recommendation system, but it also faces problems such as data sparsity, cold start, and scalability. This paper mainly proposes an improved data filling method and similarity calculation method for the inaccurate recommendation effect caused by the data sparsity problem and the cold start problem. Firstly, the user is hierarchically clustered according to the user's scoring habits, and then the users basic information such as age is used to calculate the similarity between users, and the ratio of the common scoring items is used as the weight to obtain the user similarity. Finally, the Slope-one algorithm is used to calculate the padding values of the first K similar users, and the similarity weights are added to obtain the final padding value. When calculating similarity to find the nearest neighbor set,the basic attribute of the user is used as the similarity weight,and the Sigmoid function is introduced to add the impact of the timestamp on the similarity and obtain the final similarity calculation method The experimental results show that the recommendation accuracy is significantly improved, and at the same time the data sparsity problem and the cold start problem are improved.

Key words: collaborative filtering, data sparsity, similarity, Sigmoid, scoring scale

CLC Number:

TP301

LI Kunlun, RONG Jingyue,SU Huading. An improved collaborative filtering recommendation algorithm[J]. Journal of Hebei University (Natural Science Edition), 2020, 40(1): 77-86.

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