Machine Learning
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Designing and evaluating kalas: A social navigation system for food recipes
ACM Transactions on Computer-Human Interaction (TOCHI)
Coupling feature selection and machine learning methods for navigational query identification
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Substructure similarity measurement in chinese recipes
Proceedings of the 17th international conference on World Wide Web
Finding replaceable materials in cooking recipe texts considering characteristic cooking actions
CEA '09 Proceedings of the ACM multimedia 2009 workshop on Multimedia for cooking and eating activities
Intelligent food planning: personalized recipe recommendation
Proceedings of the 15th international conference on Intelligent user interfaces
A personalized recipe advice system to promote healthful choices
Proceedings of the 16th international conference on Intelligent user interfaces
Adaptive implicit interaction for healthy nutrition and food intake supervision
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: towards mobile and intelligent interaction environments - Volume Part III
Content-boosted matrix factorization for recommender systems: experiments with recipe recommendation
Proceedings of the fifth ACM conference on Recommender systems
Uncovering the wider structure of extreme right communities spanning popular online networks
Proceedings of the 5th Annual ACM Web Science Conference
Comer, Comentar e Compartilhar: Análise de uma Rede de Ingredientes e Receitas
Proceedings of the X Brazilian Symposium in Collaborative Systems
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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.