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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
Li Jie1(), Yang Fang1, Xu Chenxi2
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 words： Personalized Recommendation      Temporal Dynamics      Sequential Patterns      Collaborative Filtering      Hot Coefficient     
Received: 24 August 2017      Published: 15 August 2018
ZTFLH:  TP311  
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Jie Li
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Cite this article:

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

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

TopN 相似度函数 准确率 召回率 F值
5 皮尔逊 7.25% 6.33% 6.76%
余弦相似 7.98% 8.06% 8.02%
修正余弦 8.26% 8.11% 8.18%
本文改进函数 8.24% 9.34% 8.76%
10 皮尔逊 10.38% 9.96% 10.17%
余弦相似 11.24% 10.01% 10.59%
修正余弦 12.22% 11.88% 12.05%
本文改进函数 13.58% 11.79% 12.62%
20 皮尔逊 10.38% 10.48% 10.43%
余弦相似 11.24% 10.84% 11.04%
修正余弦 11.98% 12.65% 12.43%
本文改进函数 11.79% 13.05% 12.39%
TopN 相似度函数 序列模式 准确率 召回率 F值
10 本文改进函数 使用 14.11% 11.68% 12.78%
不使用 13.58% 11.79% 12.62%
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