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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (7): 72-80    DOI: 10.11925/infotech.2096-3467.2017.0857
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A Personalized Recommendation Algorithm with Temporal Dynamics and Sequential Patterns
Jie Li1(),Fang Yang1,Chenxi Xu2
1School of Economics and Management, Hebei University of Technology, Tianjin 300401, China
2Jingdong Group, Beijing 100176, China
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Abstract  

[Objective] This study is to improve the effectiveness of merchandise recommendation based on temporal dynamics and sequential patterns of sales. [Methods] We developed an improved personalized recommendation algorithm for electronic commerce. First, we introduced a new similarity calculation function with time and hot coefficients. Then, we proposed an algorithm with the two-item sequential pattern, which modified the recommended list based on the sequential patterns. [Results] We examined the new method with book review data of Amazon.com from 2004-2005, and found its precision and F values were 1.89% and 0.73% higher than the collaborative filtering algorithm with adjusted cosine similarity. [Limitations] The proposed model did not examine the violations of consumers’ review scores. [Conclusions] Both the similarity function and sequential patterns can improve the effectiveness of personalized recommendation algorithms for e-commerce.

Key wordsPersonalized Recommendation      Temporal Dynamics      Sequential Patterns      Collaborative Filtering      Hot Coefficient     
Received: 24 August 2017      Published: 15 August 2018

Cite this article:

Jie Li,Fang Yang,Chenxi Xu. A Personalized Recommendation Algorithm with Temporal Dynamics and Sequential Patterns. Data Analysis and Knowledge Discovery, 2018, 2(7): 72-80.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.0857     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I7/72

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