Name
Abstract | ||
|---|---|---|
In culture analytics, it is important to ask fundamental questions that address salient characteristics of collective human behavior. This paper explores how analyzing cooking recipes in aggregate and at scale identifies these characteristics in the cooking culture, and answer fundamental questions like 'what makes a chocolate chip cookie a chocolate chip cookie?'. Aspiring cooks, professional chefs and cooking hobbyists share their recipes online resulting in thousands of different procedural instructions towards a shared goal. However, existing approaches focus merely on analysis at the ingredient level, for example, extracting ingredient information from individual recipes. We introduce RecipeScape, a prototype interface which supports visually querying, browsing and comparing cooking recipes at scale. We also present the underlying computational pipeline of RecipeScape that scrapes recipes online, extracts their ingredient and instruction information, constructs a graphical representation, and computes similarity between pairs of recipes. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3027063.3053118 | CHI Extended Abstracts |
Field | DocType | Citations |
Data science,World Wide Web,Computer science,Ingredient,Analytics,Salient | Conference | 2 |
PageRank | References | Authors |
0.39 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minsuk Chang | 1 | 5 | 5.16 |
| Vivian M. Hare | 2 | 2 | 0.39 |
| Juho Kim | 3 | 632 | 68.72 |
| Maneesh Agrawala | 4 | 5192 | 333.08 |