  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%.
