A Study on Input Methods of User Preference for Personalized Fashion Coordinate Recommendations

被引:2
作者
Hattori, Shun [1 ]
Miyamoto, Shohei [1 ]
Sunayama, Wataru [1 ]
Takahara, Madoka [2 ]
机构
[1] Univ Shiga Prefecture, Hikone, Japan
[2] Ryukoku Univ, Otsu, Shiga, Japan
来源
HUMAN INTERFACE AND THE MANAGEMENT OF INFORMATION, PT III, HIMI 2024 | 2024年 / 14691卷
关键词
Preference Input Methods; Fashion Coordinates; Personalization; Recommender Systems; CLIP; Text-to/from-Image;
D O I
10.1007/978-3-031-60125-5_12
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
In most of the existing researches and practical e-commerce services for fashion coordinate recommendations, only one of various input methods of a user's preference has been adopted without enough arguments. Therefore, this paper conducts a deeper study on various input methods of a user's preference for personalized fashion coordinate recommendations, comprehensively by two kinds of questionnaire investigation: how well do you think that you have expressed your preference to a recommender system of fashion coordinates via an input method? and how well has a recommender system recommended fashion coordinates for you based on your preference expressed via an input method?.
引用
收藏
页码:178 / 196
页数:19
相关论文
共 44 条
  • [11] Beyond Data: From User Information to Business Value through Personalized Recommendations and Consumer Science
    Amatriain, Xavier
    PROCEEDINGS OF THE 22ND ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM'13), 2013, : 2201 - 2207
  • [12] Twitter User Modeling Based on Indirect Explicit Relationships for Personalized Recommendations
    Alshammari, Abdullah
    Kapetanakis, Stelios
    Polatidis, Nikolaos
    Evans, Roger
    Alshammari, Gharbi
    COMPUTATIONAL COLLECTIVE INTELLIGENCE, PT I, 2019, 11683 : 93 - 105
  • [13] User Modeling on Twitter with WordNet Synsets and DBpedia Concepts for Personalized Recommendations
    Piao, Guangyuan
    Breslin, John G.
    CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, 2016, : 2057 - 2060
  • [14] A Trust-Aware System for Personalized User Recommendations in Social Networks
    Eirinaki, Magdalini
    Louta, Malamati D.
    Varlamis, Iraklis
    IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2014, 44 (04): : 409 - 421
  • [15] ROUND: Walking on an object-user heterogeneous network for personalized recommendations
    Gan, Mingxin
    Jiang, Rui
    EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (22) : 8791 - 8804
  • [16] Personalized Mobile App Recommendation: Reconciling App Functionality and User Privacy Preference
    Liu, Bin
    Kong, Deguang
    Cen, Lei
    Gong, Neil Zhenqiang
    Jin, Hongxia
    Xiong, Hui
    WSDM'15: PROCEEDINGS OF THE EIGHTH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, 2015, : 315 - 324
  • [17] Personalized Fashion Recommendations for Diverse Body Shapes with Contrastive Multimodal Cross-Attention Network
    Ma, Jianghong
    Sun, Huiyue
    Yang, Dezhao
    Zhang, Haijun
    ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2024, 15 (04)
  • [18] EFFECTS OF PERSONALIZED RECOMMENDATIONS VERSUS AGGREGATE RATINGS ON POST-CONSUMPTION PREFERENCE RESPONSES
    Adomavicius, Gediminas
    Bockstedt, Jesse C.
    Curley, Shawn P.
    Zhang, Jingjing
    MIS QUARTERLY, 2022, 46 (01) : 627 - 644
  • [19] EFFECTS OF PERSONALIZED RECOMMENDATIONS VERSUS AGGREGATE RATINGS ON POST-CONSUMPTION PREFERENCE RESPONSES
    Adomavicius G.
    Bockstedt J.C.
    Curley S.P.
    Zhang J.
    MIS Quarterly: Management Information Systems, 2022, 46 (01): : 627 - 644
  • [20] A personalized network-based recommendation approach via distinguishing user's preference
    Wu, Wei
    Zhang, Ruoxi
    Liu, Lianggui
    INTERNATIONAL JOURNAL OF MODERN PHYSICS B, 2019, 33 (06):