skip to main content
10.1145/3539637.3557058acmconferencesArticle/Chapter ViewAbstractPublication PageswebmediaConference Proceedingsconference-collections
research-article

Introducing Contextual Information in an Ensemble Recommendation System for Fashion Domains

Published: 07 November 2022 Publication History

Abstract

In online marketing environments, we have seen strong growth in the fashion domain, allowing consumers to access a worldwide network of brands. Despite the significant advances of the so-called Recommender Systems in more traditional scenarios, they still fail to offer a personalized and reliable fashion shopping experience that allows customers to discover products that suit their style and products that complement their choices or challenge them with new ideas. In this work, we propose a new ensemble recommendation system that combines different context information (customer-product interaction, item characteristics and user behaviour) with the predictions (recommendations) of different state-of-the-art traditional Recommender Systems to recognize new patterns in user-item interaction and to ensure a desirable level of personalization for fashion domains. Specifically, in the present work, we present a first instantiation that combines a collaborative filtering neural network method, a non-customized classical method and domain context information. In our experimental evaluation, considering two Amazon data collections, the instantiation of our proposal presented significant gains of up to 80% of MRR, 70% of NDCG and 108% of Hits compared with the methods considered state-of-the-art for the fashion recommendation scenario.

References

[1]
Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering 17, 6(2005), 734–749.
[2]
Gediminas Adomavicius and Alexander Tuzhilin. 2011. Context-aware recommender systems. In Recommender systems handbook. Springer, 217–253. https://doi.org/10.1007/978-0-387-85820-3_7
[3]
Smita Akshita and A Smita. 2013. Recommender system: review. International Journal of Computer Applications 71, 24(2013), 38–42. https://doi.org/10.5120/12693-9180
[4]
Xinlong Bao, Lawrence Bergman, and Rich Thompson. 2009. Stacking recommendation engines with additional meta-features. In Proceedings of the third ACM conference on Recommender systems. 109–116. https://doi.org/10.1145/1639714.1639734
[5]
Jesús Bobadilla, Fernando Ortega, Antonio Hernando, and Abraham Gutiérrez. 2013. Recommender systems survey. Knowledge-Based Systems 46 (2013), 109–132. https://doi.org/10.1016/j.knosys.2013.03.012
[6]
Yuri M Brovman, Marie Jacob, Natraj Srinivasan, Stephen Neola, Daniel Galron, Ryan Snyder, and Paul Wang. 2016. Optimizing similar item recommendations in a semi-structured marketplace to maximize conversion. In Proceedings of the 10th ACM Conference on Recommender Systems. 199–202. https://doi.org/10.1145/2959100.2959166
[7]
Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, 2016. Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems. 7–10. https://doi.org/10.1145/2988450.2988454
[8]
Min Dong, Xianyi Zeng, Ludovic Koehl, and Junjie Zhang. 2020. An interactive knowledge-based recommender system for fashion product design in the big data environment. Information Sciences 540(2020), 469–488. https://doi.org/10.1016/j.ins.2020.05.094
[9]
[9] Farfetch.2021. https://www.ffrecschallenge.com/ecmlpkdd2021/
[10]
Dariusz Frejlichowski, Piotr Czapiewski, and Radosław Hofman. 2016. Finding similar clothes based on semantic description for the purpose of fashion recommender system. In Asian Conference on Intelligent Information and Database Systems. Springer, 13–22. https://doi.org/10.1007/978-3-662-49381-6_2
[11]
Khalid Haruna, Maizatul Akmar Ismail, Suhendroyono Suhendroyono, Damiasih Damiasih, Adi Cilik Pierewan, Haruna Chiroma, and Tutut Herawan. 2017. Context-aware recommender system: A review of recent developmental process and future research direction. Applied Sciences 7, 12 (2017), 1211. https://doi.org/10.3390/app7121211
[12]
Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In proceedings of the 25th international conference on world wide web. 507–517. https://doi.org/10.1145/2872427.2883037
[13]
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. https://doi.org/10.1145/3397271.3401063 arxiv:2002.02126 [cs.IR]
[14]
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web. 173–182. https://doi.org/10.1145/3038912.3052569
[15]
Alexandre Heuillet, Fabien Couthouis, and Natalia Díaz-Rodríguez. 2021. Explainability in deep reinforcement learning. Knowledge-Based Systems 214 (2021), 106685. https://doi.org/10.1016/j.knosys.2020.106685
[16]
Yang Hu, Xi Yi, and Larry S Davis. 2015. Collaborative fashion recommendation: A functional tensor factorization approach. In Proceedings of the 23rd ACM international conference on Multimedia. 129–138. https://doi.org/10.1145/2733373.2806239
[17]
Dietmar Jannach and Michael Jugovac. 2019. Measuring the business value of recommender systems. ACM Transactions on Management Information Systems (TMIS) 10, 4(2019), 1–23. https://doi.org/10.1145/3370082
[18]
Wang-Cheng Kang, Chen Fang, Zhaowen Wang, and Julian McAuley. 2017. Visually-aware fashion recommendation and design with generative image models. In 2017 IEEE International Conference on Data Mining (ICDM). IEEE, 207–216. https://doi.org/10.1109/ICDM.2017.30
[19]
Shah Khusro, Zafar Ali, and Irfan Ullah. 2016. Recommender systems: issues, challenges, and research opportunities. In Information Science and Applications (ICISA) 2016. Springer, 1179–1189. https://doi.org/10.1007/978-981-10-0557-2_112
[20]
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014). https://doi.org/10.48550/arXiv.1412.6980
[21]
Joseph A Konstan and John Riedl. 2012. Recommender systems: from algorithms to user experience. User modeling and user-adapted interaction 22, 1 (2012), 101–123. https://doi.org/10.1007/s11257-011-9112-x
[22]
Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. 426–434. https://doi.org/10.1145/1401890.1401944
[23]
Dokyun Lee and Kartik Hosanagar. 2014. Impact of recommender systems on sales volume and diversity. . (2014).
[24]
Hai Thanh Nguyen, Thomas Almenningen, Martin Havig, Herman Schistad, Anders Kofod-Petersen, Helge Langseth, and Heri Ramampiaro. 2014. Learning to rank for personalised fashion recommender systems via implicit feedback. In Mining Intelligence and Knowledge Exploration. Springer, 51–61. https://doi.org/10.1007/978-3-319-13817-6_6
[25]
Al Mamunur Rashid, Istvan Albert, Dan Cosley, Shyong K Lam, Sean M McNee, Joseph A Konstan, and John Riedl. 2002. Getting to know you: learning new user preferences in recommender systems. In Proceedings of the 7th international conference on Intelligent user interfaces. 127–134. https://doi.org/10.1145/502716.502737
[26]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618(2012). https://doi.org/10.48550/arXiv.1205.2618
[27]
Francesco Ricci, Lior Rokach, and Bracha Shapira. 2011. Introduction to recommender systems handbook. In Recommender systems handbook. Springer, 1–35. https://doi.org/10.1007/978-0-387-85820-3_1
[28]
Gilbert W Stewart. 1993. On the early history of the singular value decomposition. SIAM review 35, 4 (1993), 551–566. https://doi.org/10.1137/1035134
[29]
Hind Taud and JF Mas. 2018. Multilayer perceptron (MLP). In Geomatic approaches for modeling land change scenarios. Springer, 451–455. https://doi.org/10.1007/978-3-319-60801-3_1
[30]
Yuka Wakita, Kenta Oku, Hung-Hsuan Huang, and Kyoji Kawagoe. 2015. A fashion-brand recommender system using brand association rules and features. In 2015 IIAI 4th International Congress on Advanced Applied Informatics. IEEE, 719–720. https://doi.org/10.1109/IIAI-AAI.2015.230
[31]
LC Wang, XY Zeng, Ludovic Koehl, and Yan Chen. 2014. Intelligent fashion recommender system: Fuzzy logic in personalized garment design. IEEE Transactions on Human-Machine Systems 45, 1 (2014), 95–109. https://doi.org/10.1109/THMS.2014.2364398
[32]
Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised graph learning for recommendation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 726–735. https://doi.org/10.1145/3404835.3462862
[33]
Zhengzheng Xian, Qiliang Li, Gai Li, and Lei Li. 2017. New collaborative filtering algorithms based on SVD++ and differential privacy. Mathematical Problems in Engineering 2017 (2017). https://doi.org/10.1155/2017/1975719
[34]
Ruiping Yin, Kan Li, Jie Lu, and Guangquan Zhang. 2019. Enhancing fashion recommendation with visual compatibility relationship. In The World Wide Web Conference. 3434–3440. https://doi.org/10.1145/3308558.3313739
[35]
Xianyi Zeng, Ludovic Koehl, Lichuan Wang, and Yan Chen. 2013. An intelligent recommender system for personalized fashion design. In 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS). IEEE, 760–765. https://doi.org/10.1109/IFSA-NAFIPS.2013.6608496

Index Terms

  1. Introducing Contextual Information in an Ensemble Recommendation System for Fashion Domains

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    WebMedia '22: Proceedings of the Brazilian Symposium on Multimedia and the Web
    November 2022
    389 pages
    ISBN:9781450394093
    DOI:10.1145/3539637
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    In-Cooperation

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 07 November 2022

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Contextual Information
    2. Ensemble
    3. Neural Collaborative Filtering
    4. Recommendation Systems

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    WebMedia '22
    WebMedia '22: Brazilian Symposium on Multimedia and Web
    November 7 - 11, 2022
    Curitiba, Brazil

    Acceptance Rates

    Overall Acceptance Rate 270 of 873 submissions, 31%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 56
      Total Downloads
    • Downloads (Last 12 months)5
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 13 Feb 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media