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.
翻译:在基于时装的建议设置中,纳入项目图像特征被视为一个关键因素,并表明许多传统模型的重大改进,包括但不限于矩阵要素化、自动编码器和近邻模型。虽然有许多基于图像的建议方法,使用专门的深神经网络,但与属性识别模型的比较往往被忽略,尽管这些模型能够很容易地扩展以利用项目图像特征。在本文件中,我们提出了一个简单而有效的属性识别模型,其中包括图像特征,以便在项目建议任务中更好地进行项目代表学习。拟议的模型利用了通过校准的ResNet50 组件提取的项目图像特征。我们提出了一项对比研究,将使用三种不同技术的图像特征纳入推荐系统组件,从而可以无缝地利用任何可用项目属性。关于两个基于图像的现实推荐系统数据集的实验表明,拟议的模型大大超越了所有以图像为基础的状态模型。