Towards a transfer learning approach to food recommendations through food images

Hafiz Muhammad Zubair Hasan, Hammad Khan, Talha Asif, S. Hashmi, Muhammad Rafi
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引用次数: 4

Abstract

User generated text/multimedia content are increasingly shared in online businesses systems and their effective use in user modelling and recommendation strategies is consequently growing too. In restaurant businesses the food menu along with images are a common practice and users also shared the food images they ordered and feel good about. Yelp data set challenge in their round 9 and onward introduced such rich images data for their competition. In this paper, we motivated from this rich images data of food for semantically incorporating image-specific features to the star-rating and recommendation process. We first applied a transfer learning approach with pre-trained CNNs (Convolution Neural Networks) which were used to label the Yelps food images of the restaurants using the Food101 data-set. We defined star-rating for restaurants by capturing a correlation between restaurant images and users shared images. Our proposed strategy works on discovering hidden aspects of food images and labels to be used in recommendation strategy. We performed an extensive set of experiments by creating a baseline using standard rating provided in the Yelp data-set. The proposed approach produced better Root Mean Square Error (RMSE), which is a clear indication of high-quality recommendation strategy
通过食物图像进行食物推荐的迁移学习方法
用户生成的文本/多媒体内容越来越多地在在线业务系统中共享,因此它们在用户建模和推荐策略中的有效使用也在增长。在餐饮行业,食物菜单和图片是一种常见的做法,用户也会分享他们点的食物图片,并对其感到满意。Yelp的数据集挑战在他们的第9轮及以后的比赛中引入了如此丰富的图像数据为他们的竞争。在本文中,我们从这些丰富的食物图像数据出发,在语义上将图像特定的特征结合到星级评价和推荐过程中。我们首先应用了一种迁移学习方法,使用预训练的cnn(卷积神经网络)来标记使用Food101数据集的Yelps餐馆的食物图像。我们通过捕捉餐厅图片和用户分享的图片之间的相关性来定义餐厅的星级评级。我们提出的策略是发现食物图像和标签的隐藏方面,用于推荐策略。通过使用Yelp数据集中提供的标准评级创建基线,我们执行了一组广泛的实验。该方法产生了更好的均方根误差(RMSE),这是高质量推荐策略的明确标志
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