Abstract
In fashion-based recommendation settings, incorporating the item image features is considered a crucial factor, and it has shown significant improvements to many traditional models, including but not limited to matrix factorization, auto-encoders, and nearest neighbor models. While there are numerous image-based recommender approaches that utilize dedicated deep neural networks, comparisons to attribute-aware models are often disregarded despite their ability to be easily extended to leverage items’ image features. In this paper, we propose a simple yet effective attribute-aware model that incorporates image features for better item representation learning in item recommendation tasks. The proposed model utilizes items’ image features extracted by a calibrated ResNet50 component. We present an ablation study to compare incorporating the image features using three different techniques into the recommender system component that can seamlessly leverage any available items’ attributes. Experiments on two image-based real-world recommender systems datasets show that the proposed model significantly outperforms all state-of-the-art image-based models.
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References
Costa FSd, Dolog P (2019) Collective embedding for neural context-aware recommender systems. In: Proceedings of the 13th ACM conference on recommender systems, pp 201–209
Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) ImageNet: a large-scale hierarchical image database. In: CVPR09
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
He R, McAuley J (2015) VBPR: visual Bayesian personalized ranking from implicit feedback. arXiv:1510.01784
Hou M, Wu L, Chen E, Li Z, Zheng VW, Liu Q (2019) Explainable fashion recommendation: a semantic attribute region guided approach. arXiv:1905.12862
Kang WC, Fang C, Wang Z, McAuley J (2017) Visually-aware fashion recommendation and design with generative image models. In: 2017 IEEE international conference on data mining (ICDM). IEEE, pp 207–216
Krichene W, Rendle S (2020) On sampled metrics for item recommendation. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1748–1757
Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International conference on machine learning, pp 1188–1196
Liu Q, Wu S, Wang L (2017) Deepstyle: Learning user preferences for visual recommendation. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, pp 841–844
McAuley J, Targett C, Shi Q, Van Den Hengel A (2015) Image-based recommendations on styles and substitutes. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, pp 43–52
Rashed A, Grabocka J, Schmidt-Thieme L (2019) Attribute-aware non-linear co-embeddings of graph features. In: Proceedings of the 13th ACM conference on recommender systems, pp 314–321
Rendle S (2010) Factorization machines. ICDM
Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2012) BPR: Bayesian personalized ranking from implicit feedback. arXiv:1205.2618
Weston J, Bengio S, Usunier N (2011) WSABIE: scaling up to large vocabulary image annotation
Zhang Y, Ai Q, Chen X, Croft WB (2017) Joint representation learning for top-n recommendation with heterogeneous information sources. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 1449–1458
Acknowledgements
This work is co-funded by the industry Project “IIP-Ecosphere: Next Level Ecosphere for Intelligent Industrial Production”.
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Elsayed, S., Brinkmeyer, L., Schmidt-Thieme, L. (2023). End-to-End Image-Based Fashion Recommendation. In: Corona Pampín, H.J., Shirvany, R. (eds) Recommender Systems in Fashion and Retail. RECSYS 2022. Lecture Notes in Electrical Engineering, vol 981. Springer, Cham. https://doi.org/10.1007/978-3-031-22192-7_7
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DOI: https://doi.org/10.1007/978-3-031-22192-7_7
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