RecipeScape: An Interactive Tool for Analyzing Cooking Instructions at Scale

For cooking professionals and culinary students, understanding cooking instructions is an essential yet demanding task. Common tasks include categorizing different approaches to cooking a dish and identifying usage patterns of particular ingredients or cooking methods, all of which require extensive browsing and comparison of multiple recipes. However, no existing system provides support for such in-depth and at-scale analysis. We present RecipeScape, an interactive system for browsing and analyzing the hundreds of recipes of a single dish available online. We also introduce a computational pipeline that extracts cooking processes from recipe text and calculates a procedural similarity between them. To evaluate how RecipeScape supports culinary analysis at scale, we conducted a user study with cooking professionals and culinary students with 500 recipes for two different dishes. Results show that RecipeScape clusters recipes into distinct approaches, and captures notable usage patterns of ingredients and cooking actions.

[1]  Björn Hartmann,et al.  Delta: a tool for representing and comparing workflows , 2012, CHI.

[2]  Kaizhong Zhang,et al.  On the Editing Distance Between Unordered Labeled Trees , 1992, Inf. Process. Lett..

[3]  Ben Shneiderman,et al.  Improving Healthcare with Interactive Visualization , 2013, Computer.

[4]  Albert-László Barabási,et al.  Flavor network and the principles of food pairing , 2011, Scientific reports.

[5]  Ben Shneiderman,et al.  Cohort Comparison of Event Sequences with Balanced Integration of Visual Analytics and Statistics , 2015, IUI.

[6]  Amaia Salvador,et al.  Learning Cross-Modal Embeddings for Cooking Recipes and Food Images , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Kaizhong Zhang,et al.  Simple Fast Algorithms for the Editing Distance Between Trees and Related Problems , 1989, SIAM J. Comput..

[8]  Chanchal Kumar Roy,et al.  Comparison and evaluation of code clone detection techniques and tools: A qualitative approach , 2009, Sci. Comput. Program..

[9]  Ben Shneiderman,et al.  Finding Similar People to Guide Life Choices: Challenge, Design, and Evaluation , 2017, CHI.

[10]  Maneesh Agrawala,et al.  RecipeScape: Mining and Analyzing Diverse Processes in Cooking Recipes , 2017, CHI Extended Abstracts.

[11]  Kush R. Varshney,et al.  Flavor Pairing in Medieval European Cuisine: A Study in Cooking with Dirty Data , 2013, ArXiv.

[12]  Lav R. Varshney,et al.  Computational creativity for culinary recipes , 2014, CHI Extended Abstracts.

[13]  S. C. Johnson Hierarchical clustering schemes , 1967, Psychometrika.

[14]  Albert Díaz-Guilera,et al.  Food-Bridging: A New Network Construction to Unveil the Principles of Cooking , 2017, Front. ICT.

[15]  Dong-Guk Shin,et al.  Nodal distance algorithm: calculating a phylogenetic tree comparison metric , 2003, Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings..

[16]  Lada A. Adamic,et al.  Recipe recommendation using ingredient networks , 2011, WebSci '12.

[17]  Ziheng Yang,et al.  PAML: a program package for phylogenetic analysis by maximum likelihood , 1997, Comput. Appl. Biosci..

[18]  N. Saitou,et al.  The neighbor-joining method: a new method for reconstructing phylogenetic trees. , 1987, Molecular biology and evolution.

[19]  Björn Hartmann,et al.  Browsing and Analyzing the Command-Level Structure of Large Collections of Image Manipulation Tutorials , 2013 .

[20]  Maneesh Agrawala,et al.  Comparing and managing multiple versions of slide presentations , 2006, UIST.

[21]  Michael C. Hout,et al.  Multidimensional Scaling , 2003, Encyclopedic Dictionary of Archaeology.

[22]  Yiting Wang,et al.  PathViewer: Visualizing Pathways through Student Data , 2017, CHI.

[23]  Fan Zhang,et al.  PlateClick: Bootstrapping Food Preferences Through an Adaptive Visual Interface , 2015, CIKM.

[24]  Shinji Kusumoto,et al.  CCFinder: A Multilinguistic Token-Based Code Clone Detection System for Large Scale Source Code , 2002, IEEE Trans. Software Eng..

[25]  Krzysztof Z. Gajos,et al.  Platemate: crowdsourcing nutritional analysis from food photographs , 2011, UIST.

[26]  Zhendong Su,et al.  DECKARD: Scalable and Accurate Tree-Based Detection of Code Clones , 2007, 29th International Conference on Software Engineering (ICSE'07).

[27]  Christian Santoni,et al.  SculptStat: Statistical Analysis of Digital Sculpting Workflows , 2016, ArXiv.

[28]  Desislava Zhekova,et al.  Do Good Recipes Need Butter ? Predicting User Ratings of Online Recipes , 2013 .

[29]  W. Torgerson Multidimensional scaling: I. Theory and method , 1952 .

[30]  Gordon M. Crippen,et al.  Note rapid calculation of coordinates from distance matrices , 1978 .

[31]  Kuo-Chung Tai,et al.  The Tree-to-Tree Correction Problem , 1979, JACM.

[32]  Nicholas A. Kraft,et al.  Cross-language Clone Detection , 2008, SEKE.

[33]  Mihai Surdeanu,et al.  The Stanford CoreNLP Natural Language Processing Toolkit , 2014, ACL.

[34]  R. L. Thorndike Who belongs in the family? , 1953 .

[35]  Aniket Kittur,et al.  CrowdScape: interactively visualizing user behavior and output , 2012, UIST.

[36]  Christoph Trattner,et al.  Plate and Prejudice: Gender Differences in Online Cooking , 2016, UMAP.