An attention-based convolutional neural network for recipe recommendation

The boom in cuisine websites has accumulated a wealth of recipe data, as well as interaction data between users and recipe. Based on these data, users can get recommendation that meet their tastes on recommendation algorithm. In this paper, we propose an attention-based convolutional neural network for recipe recommendation. Specifically, we use attention mechanism to capture users' preferences for different ingredients. At the same time, we use multi-perspectives convolution neural network to extract user features and recipe features at higher level. Furthermore, a multi-layer neural network is used to model the interaction between users and recipes according to their features. The experimental results show that our method achieves the better recommendation results compared with other traditional methods.

Automated summary

This paper proposes an attention-based convolutional neural network for recipe recommendation, which captures users' preferences for different ingredients and provides more accurate recommendations based on user preferences, recipe features, and ingredient features.