|Table of Contents|

Mitigation of Cold-Start Problem Based on Visual Features of Clothing Images(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2019年03期
Page:
15-
Research Field:
计算机工程
Publishing date:

Info

Title:
Mitigation of Cold-Start Problem Based on Visual Features of Clothing Images
Author(s):
He Yu; Shi Youqun; Tao Ran; Luo Xin
School of Computer Science and Technology,Donghua University,Shanghai 201600,China
Keywords:
collaborative filtering; matrix factorization; cold-start; visual feature
PACS:
TP391
DOI:
10.3969/j.issn.1672-1292.2019.03.003
Abstract:
The cold-start problem is a classic problem which has widely been concerned in the collaborative filtering recommendation algorithm. The problem seriously affects the recommendation quality of the collaborative filtering algorithm. This paper proposes a way to alleviate the cold-start problem by using the visual feature of the clothing product image learned by the deep convolutional neural network. The paper uses the matrix factorization model to estimate the users’ score on clothing items. In this paper,the items feature vector is calculated by a mapping function from the clothing product image visual feature to the items feature vector. The paper mentions two forms of mapping functions:K nearest neighbor mapping and linear mapping. The experimental results show that the visual feature of clothing image can effectively alleviate the cold-start problem of collaborative filtering algorithm.

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Last Update: 2019-09-30