Recipe Popularity Prediction with Deep Visual-Semantic Fusion
Recipe Popularity Prediction with Deep Visual-Semantic Fusion
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DOI：
10.1145/3132847.3133137
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Satoshi Sanjo;Marie Katsurai
中科院分区：
文献类型：
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作者：
Satoshi Sanjo;Marie Katsurai
预测用户创建的食谱的受欢迎程度具有很大的潜力，可以在食谱共享网站的几个应用程序中采用。为了确保在食谱被上传时的及时预测，需要基于食谱的内容特征（即，其视觉和语义特征）。本文提出了一种新的方法来预测食谱流行度使用深度视觉语义融合。我们首先预训练一个深度模型，该模型基于每一种模态预测食谱的受欢迎程度。我们在两个模型中插入额外的层，并连接它们的激活。最后，我们在融合特征上训练一个包含全连接（FC）层的网络，以学习更强大的特征，这些特征用于训练回归量。基于从Cookpad网站收集的超过15万个食谱进行的实验，我们提出了一个全面的比较与几个基线，以验证我们的方法的有效性。还描述了所提出的方法的最佳实践。
Predicting the popularity of user-created recipes has great potential to be adopted in several applications on recipe-sharing websites. To ensure timely prediction when a recipe is uploaded, a prediction model needs to be trained based on the recipe's content features (i.e., its visual and semantic features). This paper presents a novel approach to predicting recipe popularity using deep visual-semantic fusion. We first pre-train a deep model that predicts the popularity of recipes based on each single modality. We insert additional layers to the two models and concatenate their activations. Finally, we train a network comprising fully connected (FC) layers on the fused features to learn more powerful features, which are used for training a regressor. Based on experiments conducted on more than 150K recipes collected from the Cookpad website, we present a comprehensive comparison with several baselines to verify the effectiveness of our method. The best practice for the proposed method is also described.