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Image-Based Recommendations on Styles and Substitutes
Abstract
Humans inevitably develop a sense of the relationships between objects, some of which are based on their appearance. Some pairs of objects might be seen as being alternatives to each other (such as two pairs of jeans), while others may be seen as being complementary (such as a pair of jeans and a matching shirt). This information guides many of the choices that people make, from buying clothes to their interactions with each other. We seek here to model this human sense of the relationships between objects based on their appearance. Our approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within. We cast this as a network inference problem defined on graphs of related images, and provide a large-scale dataset for the training and evaluation of the same. The system we develop is capable of recommending which clothes and accessories will go well together (and which will not), amongst a host of other applications.
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Cited by
Deep Interest Network for Click-Through Rate Prediction
Zhou, G., Zhu, X., Song, C., Fan, Y., Zhu, H., Ma, X., Yan, Y., Jin, J., Li, H., & Gai, K. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3219823 · Cited by 2,128
Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks
Gururangan, S., Marasović, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.740 · Cited by 2,103
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
Chiang, W.-L., Liu, X., Si, S., Li, Y., Bengio, S., & Hsieh, C.-J. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330925 · Cited by 1,331
Deep Interest Evolution Network for Click-Through Rate Prediction
Zhou, G., Mou, N., Fan, Y., Pi, Q., Bian, W., Zhou, C., Zhu, X., & Gai, K. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33015941 · Cited by 1,099
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
He, R., & McAuley, J. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.9973 · Cited by 992
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Metadata
- Authors
- McAuley, J., Targett, C., Shi, Q., & van den Hengel, A.
- Venue
- Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2015-08-09
- Pages
- 43-52
- OpenAlex
- W2027731328
- Cited by
- 2,280
- References
- 0
- FWCI
- 104.95
- Type
- conference paper
- Language
- en
- Open access
- closed
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- McAuley, J., Targett, C., Shi, Q., & van den Hengel, A. (2015). Image-Based Recommendations on Styles and Substitutes. Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. https://doi.org/10.1145/2766462.2767755
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