论文标题
关于卷积自动编码器对基于图像的个性化建议系统的有效性
On the effectiveness of convolutional autoencoders on image-based personalized recommender systems
论文作者
论文摘要
推荐系统（RS）越来越多地存在于我们的日常生活中，尤其是自大数据的出现，这允许存储有关用户偏好的各种信息。个性化的RS成功应用于Netflix，Amazon或YouTube等平台。但是，在TripAdvisor等美食平台中，它们缺少，此外，我们可以找到数百万张带有用户口味的图像。本文探讨了将这些图像用作建模用户口味的信息来源的潜力，并提出了一种基于图像的分类系统，以获取个性化建议，并使用卷积自动编码器作为功能提取器。所提出的体系结构将使用用户的评论来应用于TripAdvisor数据，该评论可以定义为由用户，餐厅组成的三合会，以及用户拍摄的图像。由于数据集是高度不平衡的，因此在实验中还考虑了对少数族裔类别的数据增强。来自不同大小的三个城市（圣地亚哥de Costela，Barcelona和New York）的数据表明，使用卷积自动编码器作为特征提取器的有效性，而不是使用卷积神经网络计算的标准深度特征。
Recommender systems (RS) are increasingly present in our daily lives, especially since the advent of Big Data, which allows for storing all kinds of information about users' preferences. Personalized RS are successfully applied in platforms such as Netflix, Amazon or YouTube. However, they are missing in gastronomic platforms such as TripAdvisor, where moreover we can find millions of images tagged with users' tastes. This paper explores the potential of using those images as sources of information for modeling users' tastes and proposes an image-based classification system to obtain personalized recommendations, using a convolutional autoencoder as feature extractor. The proposed architecture will be applied to TripAdvisor data, using users' reviews that can be defined as a triad composed by a user, a restaurant, and an image of it taken by the user. Since the dataset is highly unbalanced, the use of data augmentation on the minority class is also considered in the experimentation. Results on data from three cities of different sizes (Santiago de Compostela, Barcelona and New York) demonstrate the effectiveness of using a convolutional autoencoder as feature extractor, instead of the standard deep features computed with convolutional neural networks.
