[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.
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