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Fashion image retrieval is one of the important services of e-commerce platforms, and it is also the basis of various fashion-related AI applications. Studies have shown that in a multi-modal environment (images + attribute labels), embedding items into specific attribute spaces can support more fine-grained similarity measures, which is especially suitable for fashion retrieval tasks. In this paper, we propose an attention-based attribute-guided similarity learning network (AttnFashion) for fashion image retrieval. The core of this network is an attribute-guided spatial attention module and an attribute-guided channel attention module, which correspond to the mapping between attributes and image regions, and the mapping between attributes and high-level image semantics, respectively. To make these two modules interact deeply, we design a parallel structure that allows them to share attribute embeddings and guide each other to extract specific features, which also helps to reduce the network parameters of the attention modules. An adaptive feature fusion strategy is proposed to synthesise the features extracted by the two modules. Extensive experiments show that the proposed AttnFashion performs better than current competitive networks in the field of fine-grained attribute-based fashion retrieval. [ABSTRACT FROM AUTHOR]

Extracting more information from feature interactions is essential to improve click-through rate (CTR) prediction accuracy. Although deep learning technology can help capture high-order feature interactions, the combination of features lacks interpretability. In this paper, we propose a multi-semantic feature interaction learning network (MeFiNet), which utilizes convolution operations to map feature interactions to multi-semantic spaces to improve their expressive ability and uses an improved Squeeze & Excitation method based on SENet to learn the importance of these interactions in different semantic spaces. The Squeeze operation helps to obtain the global importance distribution of semantic spaces, and the Excitation operation helps to dynamically re-assign the weights of semantic features so that both semantic diversity and feature diversity are considered in the model. The generated multi-semantic feature interactions are concatenated with the original feature embeddings and input into a deep learning network. Experiments on three public datasets demonstrate the effectiveness of the proposed model. Compared with state-of-the-art methods, the model achieves excellent performance (+ 0.18% in AUC and - 0.34% in LogLoss VS DeepFM; + 0.19% in AUC and - 0.33% in LogLoss VS FiBiNet). [ABSTRACT FROM AUTHOR]