[1]潘王蕾,何 瑛*.基于个性化推荐的服装知识图谱构建[J].服装学报,2022,7(03):275-282.
　PAN Wanglei,HE Ying*.Construction of Clothing Knowledge Graph Based onPersonalized Recommendation[J].Journal of Clothing Research,2022,7(03):275-282.
点击复制

基于个性化推荐的服装知识图谱构建()
分享到：

《服装学报》[ISSN:2096-1928/CN:32-1864/TS]

卷:
第7卷
期数:
2022年03期
页码:
275-282
栏目:
服装设计与营销
出版日期:
2022-06-30

文章信息/Info

Title:
Construction of Clothing Knowledge Graph Based onPersonalized Recommendation
作者:
潘王蕾1;  何 瑛*1; 2
1.浙江理工大学 服装学院,浙江 杭州 310018; 2.浙江理工大学 丝绸文化传承与产品设计数字化技术文化和旅游部重点实验室,浙江 杭州 310018
Author(s):
PAN Wanglei1;  HE Ying*1; 2
1. School of Fashion Design and Engineering,Zhejiang Sci-Tech University,Hangzhou 310018,China; 2. Key Laboratory of Silk Culture Inheriting and Products Design Digital Technology, Ministry of Culture and Tourism, Zhejiang Sci-Tech University,Hangzhou 310018,China
分类号:
TS 941.2
文献标志码:
A
摘要:
知识图谱作为大数据背景下信息承载及知识推理的工具,有助于解决大部分服装推荐系统中存在的用户和服装信息挖掘不全的问题。通过归纳服装个性化推荐的研究现状,对知识图谱及其相关实践应用进行概述,构建、分析服装领域知识体系,并将服装属性分为基础属性、表现属性和外在属性3个要素,其中服装外在属性中加入了以在线评论为基础,运用SnowNLP语言库得到的综合情感得分,并由此构建服装知识图谱,从中得到用户与服装、服装与服装之间的语义关系。通过连衣裙实例分析,证明知识图谱推理的可解释性,为服装个性化精准推荐提供参考。
Abstract:
As the information bearer and knowledge reasoning tool under the background of big data, knowledge graph is helpful to solve the problem that users and clothing information mining is not comprehensive enough in most clothing recommendation systems.This paper concluded the research present situation of clothing personalized recommendation,and summarized the knowledge graph and its application. It constructed and analyzed the knowledge system of clothing domain, and dividesd clothing attribute into three elements: basic attribute, performance attribute and external attribute. Among them, a comprehensive emotional score obtained by SnowNLP language library based on online comments was added to the external attributes of clothing, and the clothing knowledge graph was constructed to obtain the semantic relationship between users and clothing, clothing and clothing. This paper demonstrated the interpretability of knowledge graph inference through the example analysis of dress, and provided a reference for precise clothing personalized recommendation.

参考文献/References:

[1] YANG Z L, SU Z, YANG Y, et al. From recommendation to generation: a novel fashion clothing advising framework[C]//2018 7th International Conference on Digital Home(ICDH). Guilin: IEEE, 2018: 180-186.
[2] DENG Q Q, WANG R M, GONG Z X, et al. Research and implementation of personalized clothing recommendation algorithm[C]//2018 7th International Conference on Digital Home(ICDH).Guilin:IEEE, 2018: 219-223.
[3] 胡觉亮, 王正方, 韩曙光. 基于用户偏好的个性化服装推荐模式研究[J]. 浙江理工大学学报(社会科学版), 2018, 40(2): 136-143.
HU Jueliang, WANG Zhengfang, HAN Shuguang. Research on personalized clothing recommendation mode based on user preference[J]. Journal of Zhejiang Sci-Tech University(Social Sciences Edition), 2018, 40(2): 136-143.(in Chinese)
[4] 单毓馥, 李丙洋. 电子商务推荐系统中服装推荐问题研究[J]. 毛纺科技, 2016, 44(5): 66- 69.
SHAN Yufu, LI Bingyang. Researeh on apparel recommendation in e-commerce recommender systems[J]. Wool Textile Journal, 2016, 44(5): 66- 69.(in Chinese)
[5] 王义, 马尚才. 基于用户行为的个性化推荐系统的设计与应用[J]. 计算机系统应用, 2010, 19(8): 29-33.
WANG Yi, MA Shangcai. Design and application of personalized recommendation system based on users behavior[J]. Computer Systems and Applications, 2010, 19(8): 29-33.(in Chinese)
[6] 艾黎. 基于商品属性与用户聚类的个性化服装推荐研究[J]. 现代情报, 2015, 35(9): 165-170.
AI Li. The research on personalized recommendation based on commodity attribute and user clustering[J]. Journal of Modern Information, 2015, 35(9): 165-170.(in Chinese)
[7] 蔡丽玲, 季晓芬, 庞琛. 服装在线评论有用性的影响因素[J]. 纺织学报, 2018, 39(8): 158-163.
CAI Liling, JI Xiaofen, PANG Chen. Influence factors of helpfulness of online review on garment products[J]. Journal of Textile Research, 2018, 39(8): 158-163.(in Chinese)
[8] 李鑫柏, 吴鑫然, 岳昆. 基于贝叶斯网的开放世界知识图谱补全[J]. 计算机工程, 2021, 47(6): 104-114.
LI Xinbai, WU Xinran, YUE Kun. Open-world know-ledge graph completion based on Bayesian network[J]. Computer Engineering, 2021, 47(6): 104-114.(in Chinese)
[9] 杜志强, 李钰, 张叶廷, 等. 自然灾害应急知识图谱构建方法研究[J]. 武汉大学学报(信息科学版), 2020, 45(9): 1344-1355.
DU Zhiqiang, LI Yu, ZHANG Yeting, et al. Knowledge graph construction method on natural disaster emergency[J]. Geomatics and Information Science of Wuhan University, 2020, 45(9): 1344-1355.(in Chinese)
[10] ZHU M, ZHEN D S. Chinese named entity recognition for clothing knowledge graph construction[J]. IOP Confe-rence Series: Materials Science and Engineering, 2019(1): 012043.
[11] WEN Y F, LIU X Q, XU B. Personalized clothing recommendation based on knowledge graph[C]//2018 International Conference on Audio, Language and Image Processing(ICALIP).Shanghai:IEEE, 2018: 1-5.
[12] 甄德胜. 基于服装领域知识图谱的推荐技术应用研究[D]. 上海: 东华大学, 2020.
[13] 刘峤, 李杨, 段宏, 等. 知识图谱构建技术综述[J]. 计算机研究与发展, 2016, 53(3): 582- 600.
LIU Qiao, LI Yang, DUAN Hong, et al. Knowledge graph construction techniques[J]. Journal of Computer Research and Development, 2016, 53(3): 582- 600.(in Chinese)
[14] 吴长亮. 消费者决策角度下的产品属性分类体系研究[J]. 商业时代, 2011(19): 24-25.
WU Changliang.Research on product attribute classification system from the perspective of consumer decision[J]. Commercial Times, 2011(19): 24-25.(in Chinese)
[15] WIRTZ J. Halo in customer satisfaction measures[J]. International Journal of Service Industry Management, 2003, 14(1): 96-119.
[16] 梁建芳, 李筱胜. 电子商务环境下女性服装消费行为分析[J]. 浙江理工大学学报, 2011, 28(5): 728-733.
LIANG Jianfang, LI Xiaosheng. Analysis on female clothing consumer behavior under e-commerce environment[J]. Journal of Zhejiang Sci-Tech University, 2011, 28(5): 728-733.(in Chinese)
[17] LIU S, LIU L Q, YAN S C. Fashion analysis: current techniques and future directions[J]. IEEE Multimedia, 2014, 21(2): 72-79.
[18] 吴苗苗, 刘骊, 付晓东, 等. 款式特征描述符的服装图像细粒度分类方法[J]. 计算机辅助设计与图形学学报, 2019, 31(5): 780-791.
WU Miaomiao, LIU Li, FU Xiaodong, et al. Fine-grained clothing image classification by style feature description[J]. Journal of Computer-Aided Design and Computer Graphics, 2019, 31(5): 780-791.(in Chinese)
[19] 汪涛, 王魁, 陈厚. 时间间隔何时能够提高在线评论的有用性感知——基于归因理论的视角[J]. 商业经济与管理, 2015(2): 46-56.
WANG Tao, WANG Kui, CHEN Hou. The impact of temporal distance on the increase of the perceived usefulness of online reviews—from the perspective of the attribution theory[J]. Journal of Business Economics, 2015(2): 46-56.(in Chinese)
[20] HUANG X W, FANG Q, QIAN S S, et al. Explainable interaction-driven user modeling over knowledge graph for sequential recommendation[C]//Proceedings of the 27th ACM International Conference on Multimedia.New York: ACM, 2019: 548-556.
[21] 袁泉, 成振华, 江洋. 基于知识图谱和协同过滤的电影推荐算法研究[J]. 计算机工程与科学, 2020, 42(4): 714-721.
YUAN Quan, CHENG Zhenhua, JIANG Yang. A movie recommendation algorithm based on knowledge graph and collaborative filtering[J]. Computer Engineering and Science, 2020, 42(4): 714-721.(in Chinese)
[22] 汤伟韬, 余敦辉, 魏世伟. 融合知识图谱与用户评论的商品推荐算法[J]. 计算机工程, 2020, 46(8): 93-100.
TANG Weitao, YU Dunhui, WEI Shiwei. Commodity recommendation algorithm fusing with knowledge graph and user comment[J]. Computer Engineering, 2020, 46(8): 93-100.(in Chinese)
[23] 王根生, 潘方正. 融合语义相似度的协同过滤推荐算法[J]. 中国科学技术大学学报, 2019, 49(10): 835-841.
WANG Gensheng, PAN Fangzheng. Collaborative filtering recommendation algorithm based on semantic similarity[J]. Journal of University of Science and Technology of China, 2019, 49(10): 835-841.(in Chinese)
(责任编辑:沈天琦)

相似文献/References:

[1]李艳梅,罗 莹.基于CiteSpace的国内外织物柔性传感器研究的可视化分析[J].服装学报,2024,9(05):448.
　LI Yanmei,LUO Ying.Visual Analysis of Domestic and International Research on Textile Flexible Sensors Based on CiteSpace[J].Journal of Clothing Research,2024,9(03):448.

更新日期/Last Update: 2022-06-30