Omprakash Sonie
ABSTRACT
Clothing we wear reveals our personal style - wealth, occupation, religion, location and socio-identity. Shopper's aesthetic preferences thus influence purchasing decision in a lifestyle marketplace. Given the image of a fashion item, recommending complementary matches is a challenge. This tutorial discusses various techniques for fashion recommendation which in turn enhance conventional data mining approaches like collaborative filtering and matrix factorization. For a few such models and methods, we outline results using real-world data from various online shopping platforms. Recent advances in deep learning are presented for compatibility modeling, learning-to-rank and explainable recommendation.
- D. Hu et al. 2014. Style in the Long Tail: Discovering Unique Interests with Latent Variable Models in Large Scale Social E-commerce. In KDD. 1640--1649. Google Scholar
Digital Library
- L. Dhakad et al. 2017. SOPER: Discovering the Influence of Fashion and the Many Faces of User from Session Logs using Stick Breaking Process. In CIKM. 1609--1618. Google ScholarDigital Library
- Q. Liu et al. 2017. DeepStyle: Learning User Preferences for Visual. In SIGIR. 841--844. Google ScholarDigital Library
- W. Yu et al. 2018. Aesthetic-based Clothing Recommendation. In WWW. 649--658. Google ScholarDigital Library
- X. Han et al. 2017. Learning Fashion Compatibility with Bidirectional LSTMs. In ACM Multimedia. 1078--1086. Google ScholarDigital Library
- Y. Hu et al. 2015. Collaborative Fashion Recommendation: A Functional Tensor Factorization Approach. In ACM Multimedia. 129--138. Google ScholarDigital Library
- Z. Wang et al. 2018. A Path-constrained Framework for Discriminating Substitutable and Complementary Products in E-commerce. In WSDM. 619--627. Google ScholarDigital Library
- R. He and J. McAuley. 2016. Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In WWW. 507--517. Google ScholarDigital Library
Index Terms
- Personalised Fashion Recommendation using Deep Learning
Recommendations
Personalized Fashion Recommendation with Visual Explanations based on Multimodal Attention Network: Towards Visually Explainable Recommendation
SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information RetrievalFashion recommendation has attracted increasing attention from both industry and academic communities. This paper proposes a novel neural architecture for fashion recommendation based on both image region-level features and user review information. Our ...
POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion
KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningIncreasing demand for fashion recommendation raises a lot of challenges for online shopping platforms and fashion communities. In particular, there exist two requirements for fashion outfit recommendation: the Compatibility of the generated fashion ...
Collaborative Learning Using LSTM-RNN for Personalized Recommendation
Services Computing – SCC 2020AbstractToday, the ability to track users’ sequence of online activities, makes identifying their evolving preferences for recommendation practicable. However, despite the myriad of available online activity information, most existing time-based ...
Comments