纺织学报 ›› 2023, Vol. 44 ›› Issue (03): 176-186.doi: 10.13475/j.fzxb.20211106611

• 服装工程 • 上一篇    下一篇

基于多模态融合的个性化服装搭配推荐

刘军平1,2,3, 张伏红1, 胡新荣1,2,3(), 彭涛1,2,3, 李丽1,2,3, 朱强1,2,3, 张俊杰1,2,3   

  1. 1.武汉纺织大学 计算机与人工智能学院, 湖北 武汉 430200
    2.纺织服装智能化湖北省工程研究中心, 湖北 武汉 430200
    3.湖北省服装信息化工程技术研究中心, 湖北 武汉 430200
  • 收稿日期:2021-11-12 修回日期:2022-10-23 出版日期:2023-03-15 发布日期:2023-04-14
  • 通讯作者: 胡新荣(1973—),女,教授,博士。主要研究方向为图形图像处理、计算机视觉。E-mail:hxr@wtu.edu.cn。
  • 作者简介:刘军平(1979—),男,副教授,博士。主要研究方向为工业大数据、人工智能。
  • 基金资助:
    湖北省高等学校优秀中青年科技创新团队计划项目(T201807);湖北省教育厅科学研究计划重点项目(D20191708)

Personalized clothing matching recommendation based on multi-modal fusion

LIU Junping1,2,3, ZHANG Fuhong1, HU Xinrong1,2,3(), PENG Tao1,2,3, LI Li1,2,3, ZHU Qiang1,2,3, ZHANG Junjie1,2,3   

  1. 1. College of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, Hubei 430200, China
    2. Hubei Provincial Engineering Research Center for Intelligent Textile and Fashion, Wuhan Textile University, Wuhan, Hubei 430200, China
    3. Engineering Research Center of Hubei Province for Clothing Information, Wuhan Textile University, Wuhan, Hubei 430200, China
  • Received:2021-11-12 Revised:2022-10-23 Published:2023-03-15 Online:2023-04-14

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摘要：

为提高服装的匹配度且实现高精度推荐,从而满足消费者对个性化服装搭配推荐的巨大需求,研究了从服装颜色到类别的高度非线性复杂属性交互,并以服装搭配的匹配度量化标准为基础,构建了单品潜在特征表示空间的嵌入模型,通过构建融合多模态信息的矩阵分解框架模型,进一步分析了现有多模态特征融合算法的不足,刻画了不同用户的服装风格偏好,通过特征提取、多模态特征融合、匹配度计算等手段建立个性化服装搭配方案。实验结果表明:该模型计算出的服装匹配度达到了0.81,相较于传统方法提高了1.25%,实现了更高准确度和推荐精度的个性化服装推荐。

关键词: 服装搭配, 个性化推荐, 多模态, 特征提取, 特征融合, 匹配度

Abstract:

Objective In the context of fast fashion, most consumers do not have the keen insight of professional designers on fashion clothing matching, which leads to their capability of quickly selecting a set of appropriate, harmonious and suitable clothing from a large number of clothing. In order to better improve users' online shopping experience and help them accurately express their unique personality characteristics, professional identity, status and other image positioning to the outside world, this paper aims to achieve high-precision recommendation by improving the clothing matching degree, so as to meet the huge demand of consumers for personalized clothing matching recommendation.

Method By studying the highly nonlinear complex attribute interaction from clothing color to category, and based on the quantitative standard of matching degree of clothing matching, an embedded model of the potential feature representation space of an item was built. By building a matrix decomposition framework model that integrates multimodal information, the shortcomings of existing multimodal feature fusion algorithms were further analyzed, and the clothing style preferences of different users were depicted. Through feature extraction, multimodal feature fusion match degree was calculated and other operations were carried out to establish personalized clothing matching scheme.

Results PCMF (personalized clothing matching recommendation based on multi-modal fusion) with some conventional clothing matching methods were qualitatively compared. Compared with all baselines, the clothing matching degree calculated by this model reached 0.81, which is 1.25% higher than the AUC (area under curve) value of conventional methods (Tab.2). It is confirmed that the transposition fusion method of text features and visual features used in PCMF improves the correlation between features, making the presentation of individual style more accurate. In order to compare the difference of contribution of different modal information to the matching degree of PCMF modeled clothing, experiments under three different modal combinations were conducted, i.e. PCMF-T (only exploring the text information of items), PCMF-V (only exploring the visual information of items), and PCMF-TV (exploring the visual and text information of items) (Tab.3). The AUC value of PCMF-T reached 0.775, higher than that of PCMF-V (0.763), indicating that the text information of the piece can more succinctly summarize the key features of the piece, such as patterns, materials and brands. PCMF-TV shows better performance than PCMF-T and PCMF-V, which indicates the necessity of combining multi-modal information of items, and verifies the effectiveness of adding user factors to the general clothing matching modeling to make personalized clothing matching recommendation. In order to effectively evaluate the practical application of the PCMF model, the PCMF model was deployed in the complementary item retrieval task (Fig.5). It is demonstrated that the PCMF model can complete the personalized clothing matching recommendation task according to the user's preferences. In addition, the MRR (mean reciprocal rank) measurement method was used as the evaluation index to further evaluate the model. PCMF performs better than other models regardless of the number of clothing candidates (Fig.6).

Conclusion Through the combination of IGCM (item-item general clothing match modeling) and UPCM (user-item personalized clothing match modeling), a personalized clothing matching recommendation model based on multi-modal fusion is constructed, which facilitates high-precision personalized clothing matching recommendation. Specifically, the purpose is to match a lower garment that not only has a good match with a given user's top, but also meets the user's taste. In general, the research results show the necessity and effectiveness of combining visual and text modal information and introducing user factors in personalized clothing matching recommendation and the practical application value of PCMF in real scenes is verified. In the future, the clothing matching recommendation problem of two examples will be transformed into a multi-instance learning problem to provide users with personalized package recommendation including shoes and accessories.

Key words: complementary clothing matching, personalized recommendation, multi-modal, feature extraction, feature fusion, matching degree

中图分类号: 

  • TS941.26

图1

基于多模态融合的个性化服装搭配推荐模型PCMF结构"

表1

数据集IQON 3000的数据统计"

单品种类 数量/件
外套 35 765
上衣 119 895
下衣 77 813
连衣裙 25 816
鞋子 106 598
配饰 306 448

图2

单品的视觉特征抽取过程"

图3

单品的文本特征抽取过程"

表2

不同方法下AUC的性能比较"

方法 AUC值
POP 0. 421
RAND 0. 501
Bi-LSTM 0. 661
BPR-DAE 0. 699
VTBPR 0. 801
GP-BPR 0. 802
PCMF 0. 814

图4

PCMF与 GP-BPR 的AUC性能比较"

表3

不同模态下AUC的性能比较"

方法 AUC值
PCMF-V 0. 763
PCMF-T 0. 775
PCMF-TV 0. 814

图5

下衣推荐排行榜"

图6

6种基准方法在T范围下的MRR性能"

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