Journal of Textile Research ›› 2021, Vol. 42 ›› Issue (12): 138-144.doi: 10.13475/j.fzxb.20210204107

• Apparel Engineering • Previous Articles     Next Articles

Improved clothing recommendation algorithm based on simulation scoring

JIANG Xuewei1,2(), TIAN Runyu1,2, LU Fangxiao3, ZHANG Yi1,2   

  1. 1. School of Fashion, Wuhan Textile University, Wuhan, Hubei 430073, China
    2. Wuhan Textile and Apparel Digital Engineering Technology Research Center, Wuhan Textile University, Wuhan, Hubei 430073, China
    3. School of Geodesy and Geomatics, Wuhan University, Wuhan, Hubei 430079, China
  • Received:2021-02-15 Revised:2021-09-14 Online:2021-12-15 Published:2021-12-29

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Abstract:

The traditional clothing recommendation algorithms do not pay enough attention to consumers and clothing characteristics, hence the prediction results are short in pertinence and effectiveness. To improve on these, a model of consumers' interest attenuation in shopping was constructed by using clothing coding, time interval and Euclidean distance, and an improved clothing recommendation algorithm based on simulation scoring was proposed. By comparing the average absolute error between the true values and the predicted values of the simulation scoring algorithm and the improved algorithm based on singular value decomposition, it is found that the average absolute error of the simulation scoring algorithm is 0.808, which is 0.024 lower than that of the improved algorithm based on singular value decomposition. The error of 25% of all cases is bigger than 1, and the average error after excluding this part of cases is 0.632. Through such case analysis, it is found that the average absolute accuracy of 90% recommendation is greater than 96%, and the accuracy of 10% recommendation is between 60% and 64%. The reason for big error is either because of the preference changes of the targeted consumer groups, or the targeted consumer group have not purchased clothes for a long time.

Key words: clothing recommendation algorithm, sparce data, simulation scoring, convolution neural network, Euclidean distance

CLC Number: 

  • TS941.73

Fig.1

Schematic diagram of thesaurus coding"

Tab.1

Clothing coding table"

服装
编号		 标签 一级编码 二级编码
1 韩版、原宿、纯棉、宽松、
T恤、短袖、露肩、上衣		 (1,1,1) (1,1,1,1,1,0,
0,0,0,0,0,0)
2 韩版、简约、圆领、套头、
修身、露肚脐、短袖、T恤		 (1,0,1) (1,0,0,0,0,1,
1,1,1,1,0,0)
3 宽松、T恤、原宿、韩版、
百搭、上衣、慵懒风		 (1,1,0) (1,1,1,0,0,0,
0,0,0,0,1,1)

Fig.2

Calculation process of simulation scoring algorithm"

Fig.3

Mean square error for a"

Fig.4

Mean square error for b"

Tab.2

Time fuzzification rule"

实际时间/月 0~1 1~3 3~6 6~12 >12
模糊时间 0 1 2 3 4

Fig.5

Text convolution neural network"

Tab.3

Scoring conversion rules"

转化前得分 <60 60~70 70~80 80~90 >90
转化后得分 1 2 3 4 5

Fig.6

Mean absolute error value of users"

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