The recording and sharing of cooking recipes, a human activity dating back
thousands of years, naturally became an early and prominent social use of the
web. The resulting online recipe collections are repositories of ingredient
combinations and cooking methods whose large-scale and variety yield
interesting insights about both the fundamentals of cooking and user
preferences. At the level of an individual ingredient we measure whether it
tends to be essential or can be dropped or added, and whether its quantity can
be modified. We also construct two types of networks to capture the
relationships between ingredients. The complement network captures which
ingredients tend to co-occur frequently, and is composed of two large
communities: one savory, the other sweet. The substitute network, derived from
user-generated suggestions for modifications, can be decomposed into many
communities of functionally equivalent ingredients, and captures users'
preference for healthier variants of a recipe. Our experiments reveal that
recipe ratings can be well predicted with features derived from combinations of
ingredient networks and nutrition information.

本文研究了在线食谱集合的成分组合、共现和偏好，以及它们与食谱评级之间的关系。