The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback
Open accessAbstract
Modern recommender systems model people and items by discovering or `teasing apart' the underlying dimensions that encode the properties of items and users' preferences toward them. Critically, such dimensions are uncovered based on user feedback, often in implicit form (such as purchase histories, browsing logs, etc.); in addition, some recommender systems make use of side information, such as product attributes, temporal information, or review text.However one important feature that is typically ignored by existing personalized recommendation and ranking methods is the visual appearance of the items being considered. In this paper we propose a scalable factorization model to incorporate visual signals into predictors of people's opinions, which we apply to a selection of large, real-world datasets. We make use of visual features extracted from product images using (pre-trained) deep networks, on top of which we learn an additional layer that uncovers the visual dimensions that best explain the variation in people's feedback. This not only leads to significantly more accurate personalized ranking methods, but also helps to alleviate cold start issues, and qualitatively to analyze the visual dimensions that influence people's opinions.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet Large Scale Visual Recognition Challenge
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CNN Features Off-the-Shelf: An Astounding Baseline for Recognition
Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2014.131 · Cited by 4,451
BPR: Bayesian Personalized Ranking from Implicit Feedback
Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1205.2618 · Cited by 4,242
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019
Caffe: Convolutional Architecture for Fast Feature Embedding
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Collaborative Filtering for Implicit Feedback Datasets
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Image-Based Recommendations on Styles and Substitutes
McAuley, J., Targett, C., Shi, Q., & van den Hengel, A. (2015) · Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/2766462.2767755 · Cited by 2,280
Self-citationAccelerating t-SNE using tree-based algorithms
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Methods and metrics for cold-start recommendations
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Cited by
Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited
Yuan, Z., Yuan, F., Song, Y., Li, Y., Fu, J., Yang, F., Pan, Y., & Ni, Y. (2023) · Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3539618.3591932 · Cited by 200
Anelli, V. W., Deldjoo, Y., Di Noia, T., Malitesta, D., & Merra, F. A. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3462848 · Cited by 31
Shi, L., Li, S., Ding, X., & Bu, Z. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118932 · Cited by 14
Local Factor Models for Large-Scale Inductive Recommendation
Yang, L., Schnabel, T., Bennett, P. N., & Dumais, S. (2021) · Fifteenth ACM Conference on Recommender Systems · doi:10.1145/3460231.3474276 · Cited by 6
Metadata
- Authors
- He, R., & McAuley, J.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2016-02-21
- Volume
- 30
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2963655167
- Cited by
- 992
- References
- 29
- FWCI
- 58.98
- Type
- conference paper
- Language
- en
- Open access
- diamond
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