E-Commerce Collaborative Filtering Algorithm Research Based on Cloud Computing

Wen Qing Zhao; Fei Fei Han; Rui Cai; De Wen Wang

doi:10.4028/www.scientific.net/AMR.846-847.1566

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 846-847E-Commerce Collaborative Filtering Algorithm...

E-Commerce Collaborative Filtering Algorithm Research Based on Cloud Computing

Abstract:

With the continuous development of online shopping, a day will generate tens of thousands of consumer records. E-commerce sites want to recommend the consumers that they may be interested in the products by analyzing the consumer historical consumption data. However massive consumer records led to recommendation speed getting slow by using the traditional personalized recommendation algorithm. By researching on the collaborative filtering algorithm based on ALS and the MapReduce parallel programming model, we explore parallelization of collaborative filtering algorithm based on ALS. The experimental results show that the algorithm in this paper can improve the computing efficiency.

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Periodical:

Advanced Materials Research (Volumes 846-847)

Pages:

1566-1569

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.846-847.1566

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Online since:

November 2013

Authors:

Wen Qing Zhao*, Fei Fei Han, Rui Cai, De Wen Wang

Keywords:

Collaborative Filtering (CF), Hadoop, MapReduce, Parallel

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* - Corresponding Author

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