Design of Collaborative Filtering Approach Recommendation Algorithm Based on Hadoop

doi:10.3969/j.issn.1671-1122.2022.10.013

Abstract

Abstract:

While dealing with complex computing tasks, the large number of heterogeneous data from different populations will cause abnormal values and noise in heterogeneous networks, which will lead to low performance of the recommendation algorithm easily. Thus, a personalized recommendation algorithm is proposed for such issue based on the items. Firstly, based on the Pearson correlation and cosine similarity method, the weight function of the item contribution is introduced in the similarity calculation.Secondly, according to the construction of the heterogeneous network, the similarity of two items is calculated by the design of the weight function, the insensitive performance of the outliers is realized. Finally, according to the movie data, we realized the collaborative filtering of the recommendation algorithm based on the Hadoop platform. Experimental results show that the method can effectively improve the accuracy and real-time performance of recommendation algorithm, improve the quality of network monitoring and prolong the network lifetime.

Key words: collaborative filtering, recommendation, heterogeneous network, Hadoop

CLC Number:

TP309

YU Xianrong, FAN Jiejie. Design of Collaborative Filtering Approach Recommendation Algorithm Based on Hadoop[J]. Netinfo Security, 2022, 22(10): 91-97.

Figures/Tables 8

References 15

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