Abstract
In this paper we focus on the problem of multi-label image recognition for visually-aware recommender systems. We propose a two stage approach in which a deep convolutional neural network is firstly fine-tuned on a part of the training set. Secondly, an attention-based aggregation network is trained to compute the weighted average of visual features in an input image set. Our approach is implemented as a mobile fashion recommender system application. It is experimentally show on the Amazon Fashion dataset that our approach achieves an F1-measure of 0.58 for 15 recommendations, which is twice as good as the 0.25 F1-measure for conventional averaging of feature vectors.
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Acknowledgements
The paper was prepared within the framework of the Academic Fund Program at the National Research University Higher School of Economics (HSE) in 2019 (grant No. 19-04-0004) and by the Russian Academic Excellence Project 5-100.
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Demochkin, K., Savchenko, A.V. (2019). Multi-label Image Set Recognition in Visually-Aware Recommender Systems. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2019. Lecture Notes in Computer Science(), vol 11832. Springer, Cham. https://doi.org/10.1007/978-3-030-37334-4_26
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