The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Personalized Fashion Recommendation with Visual Explanations based on Multimodal Attention Network: Towards Visually Explainable Recommendation
Open accessAbstract
Fashion 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 basic intuition is that: for a fashion image, not all the regions are equally important for the users, i.e., people usually care about a few parts of the fashion image. To model such human sense, we learn an attention model over many pre-segmented image regions, based on which we can understand where a user is really interested in on the image, and correspondingly, represent the image in a more accurate manner. In addition, by discovering such fine-grained visual preference, we can visually explain a recommendation by highlighting some regions of its image. For better learning the attention model, we also introduce user review information as a weak supervision signal to collect more comprehensive user preference. In our final framework, the visual and textual features are seamlessly coupled by a multimodal attention network. Based on this architecture, we can not only provide accurate recommendation, but also can accompany each recommended item with novel visual explanations. We conduct extensive experiments to demonstrate the superiority of our proposed model in terms of Top-N recommendation, and also we build a collectively labeled dataset for evaluating our provided visual explanations in a quantitative manner.
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(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
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
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
Recurrent Neural Network Regularization
Zaremba, W., Sutskever, I., & Vinyals, O. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.2329 · Cited by 2,123
Hidden factors and hidden topics: understanding rating dimensions with review text
McAuley, J., & Leskovec, J. (2013) · Proceedings of the 7th ACM conference on Recommender systems · doi:10.1145/2507157.2507163 · Cited by 1,564
Collaborative topic modeling for recommending scientific articles
Wang, C., & Blei, D. M. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2020408.2020480 · Cited by 1,527
He, R., & McAuley, J. (2016) · Proceedings of the 25th International Conference on World Wide Web · doi:10.1145/2872427.2883037 · Cited by 1,459
IR evaluation methods for retrieving highly relevant documents
Järvelin, K., & Kekäläinen, J. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130374 · Cited by 1,444
Neural Factorization Machines for Sparse Predictive Analytics
He, X., & Chua, T.-S. (2017) · Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3077136.3080777 · Cited by 1,340
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
Wang, H., Zhang, F., Wang, J., Zhao, M., Li, W., Xie, X., & Guo, M. (2018) · Proceedings of the 27th ACM International Conference on Information and Knowledge Management · doi:10.1145/3269206.3271739 · Cited by 1,160
Cited by
Nauta, M., Trienes, J., Pathak, S., Nguyen, E., Peters, M., Schmitt, Y., Schlötterer, J., van Keulen, M., & Seifert, C. (2023) · ACM Computing Surveys · doi:10.1145/3583558 · Cited by 590
Bootstrap Latent Representations for Multi-modal Recommendation
Zhou, X., Zhou, H., Liu, Y., Zeng, Z., Miao, C., Wang, P., You, Y., & Jiang, F. (2023) · Proceedings of the ACM Web Conference 2023 · doi:10.1145/3543507.3583251 · Cited by 298
A Review on Explainability in Multimodal Deep Neural Nets
Joshi, G., Walambe, R., & Kotecha, K. (2021) · IEEE Access · doi:10.1109/access.2021.3070212 · Cited by 204
Personalized Prompt Learning for Explainable Recommendation
Li, L., Zhang, Y., & Chen, L. (2023) · ACM Transactions on Information Systems · doi:10.1145/3580488 · Cited by 172
Self-citationGenerate Neural Template Explanations for Recommendation
Li, L., Zhang, Y., & Chen, L. (2020) · Proceedings of the 29th ACM International Conference on Information & Knowledge Management · doi:10.1145/3340531.3411992 · Cited by 149
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Metadata
- Authors
- Chen, X., Chen, H., Xu, H., Zhang, Y., Cao, Y., Qin, Z., & Zha, H.
- Venue
- Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2019-07-18
- Pages
- 765-774
- OpenAlex
- W2964258748
- Cited by
- 284
- References
- 41
- FWCI
- 21.55
- Type
- conference paper
- Language
- en
- Open access
- green
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- Chen, X., Chen, H., Xu, H., Zhang, Y., Cao, Y., Qin, Z., & Zha, H. (2019). Personalized Fashion Recommendation with Visual Explanations based on Multimodal Attention Network: Towards Visually Explainable Recommendation. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. https://doi.org/10.1145/3331184.3331254
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