Completing partial recipes using item-based collaborative filtering to recommend ingredients
This work addresses the need for healthy diet encouragement by providing recipe completion recommendations, but it is incremental as it applies existing collaborative filtering techniques to a new domain.
The paper tackled the problem of recommending ingredients to complete partial recipes using item-based collaborative filtering on a sparse dataset, achieving a recall@10 of approximately 40% with their best method.
Increased public interest in healthy lifestyles has motivated the study of algorithms that encourage people to follow a healthy diet. Applying collaborative filtering to build recommendation systems in domains where only implicit feedback is available is also a rapidly growing research area. In this report we combine these two trends by developing a recommendation system to suggest ingredients that can be added to a partial recipe. We implement the item-based collaborative filtering algorithm using a high-dimensional, sparse dataset of recipes, which inherently contains only implicit feedback. We explore the effect of different similarity measures and dimensionality reduction on the quality of the recommendations, and find that our best method achieves a recall@10 of circa 40%.