论文标题
基于图像的端到端时尚建议
End-to-End Image-Based Fashion Recommendation
论文作者
论文摘要
在基于时尚的推荐设置中，结合项目图像特征被认为是至关重要的因素，并且显示出对许多传统模型的显着改进，包括但不限于矩阵分解，自动编码器和最近的邻居模型。尽管有许多基于图像的建议方法利用专用的深神经网络，但尽管能够轻松扩展以利用项目的图像特征，但通常会忽略与属性感知模型的比较。在本文中，我们提出了一个简单而有效的属性感知模型，该模型将图像功能结合在项目推荐任务中，以学习更好的项目表示。提出的模型利用了由校准的Resnet50组件提取的项目的图像特征。我们提出了一项消融研究，以比较使用三种不同技术将图像特征纳入推荐系统组件，该组件可以无缝利用任何可用的物品的属性。在两个基于图像的现实世界推荐系统数据集上的实验表明，所提出的模型显着胜过所有基于图像的最新模型。
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.
