Abstract
Countless possibilities of recipe combinations challenge us to determine which additional ingredient goes well with others. In this work, we propose RecipeBowl which is a cooking recommendation system that takes a set of ingredients and cooking tags as input and suggests possible ingredient and recipe choices. We formulate a recipe completion task to train RecipeBowl on our constructed dataset where the model predicts a target ingredient previously eliminated from the original recipe. The RecipeBowl consists of a set encoder and a 2-way decoder for prediction. For the set encoder, we utilize the Set Transformer that builds meaningful set representations. Overall, our model builds a set representation of an leave-one-out recipe and maps it to the ingredient and recipe embedding space. Experimental results demonstrate the effectiveness of our approach. Furthermore, analysis on model predictions and interpretations show interesting insights related to cooking knowledge.
Highlights
Finding the right additional ingredients and sample recipes is an essential, yet challenging task in the culinary world due to vast cooking possibilities [1]
Both Induced Set Attention Blocks (ISAB) and pooling by Multihead Attention (PMA) layer use Multihead Attention Blocks (MAB) which are the components of the Transformer model originally proposed by Vaswani et al [27]
Given a set of n ingredient vectors refined by the previous SAB or ISAB, Z ∈ Rn×d, pooling by Multihead Attention (PMA) is expressed as follows, PMA = MAB(Z, RFF(S))
Summary
Finding the right additional ingredients and sample recipes is an essential, yet challenging task in the culinary world due to vast cooking possibilities [1]. We propose RecipeBowl, a set-based model that jointly recommends ingredients and recipes. Given lime, chicken breasts, olive oil and garlic as input set, the user desires to cook an ’easy’, ’main dish’ grilled in an ’oven’ using ’chicken’. In this case, the RecipeBowl suggests ingredients (e.g., balsamic vinegar, cilantro, white wine, rosemary and so on) that are likely to go well with the input set and satisfy the user’s needs. The authors of this work demonstrated the Set Transformer’s effectiveness by using the pre-trained embeddings for food-related downstream tasks such as cuisine classification
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