PERSONALIZED E-COMMERCE RECOMMENDATION SYSTEM BASED ON COLLABORATIVE FILTERING UNDER HADOOP
Journal Title: Topics in Intelligent Computing and Industry Design (ICID) - Year 2017, Vol 1, Issue 2
Abstract
Aiming at the disadvantages of the traditional collaborative filtering recommendation algorithm based on user rating matrix in terms of items recommendation accuracy and scalability, a personalized e-commerce recommendation system based on Hadoop is designed and implemented. The data sparseness of user-item rating matrix leads to great contingency of recommendation results, and the more sparseness scoring matrix, the worse the recommended effect. The system in this paper uses the e-commerce platform implicit user rating and explicit rating data to the user preference characteristics as the impact factor to construct and replace the rate matrix. Finally, with the modified user-item interaction information, the Top-N recommendation method is adopted to recommend the products for the target user may be interest in. Comparative experimental results show that the recommendation system can improve the recommendation accuracy of products. The experimental results on the Hadoop platform also show that using the MapReduce parallel computing framework can improve computational eff iciency and algorithm scalability.
Authors and Affiliations
Haodong Tang, Xiaohui Cheng
Keywords
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- EP ID EP409821
- DOI 10.26480/wsmce.01.2017.146.148
- Views 84
- Downloads 0
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