INDoRI: Indian Dataset of Recipes and Ingredients and its Ingredient Network

TL;DR

This paper introduces INDoRI, a comprehensive Indian culinary dataset with recipes and ingredients, along with an ingredient network, enabling analysis of culinary structures and regional diversity.

Contribution

The paper presents a new Indian culinary dataset, a domain-specific stop words collection, and constructs an ingredient network for analyzing culinary relationships.

Findings

Ingredient network reveals regional ingredient connections

Community detection identifies ingredient clusters

Dataset supports diverse culinary research

Abstract

Exploring and comprehending the culinary heritage of a nation holds a captivating allure. It offers insights into the structure and qualities of its cuisine. The endeavor becomes more accessible with the availability of a well-organized dataset. In this paper, we present the introduction of INDoRI (Indian Dataset of Recipes and Ingredients), a compilation drawn from seven distinct online platforms, representing 18 regions within the Indian subcontinent. This comprehensive geographical span ensures a portrayal of the rich variety within culinary practices. Furthermore, we introduce a unique collection of stop words, referred to as ISW (Ingredient Stop Words), manually tuned for the culinary domain. We assess the validity of ISW in the context of global cuisines beyond Indian culinary tradition. Subsequently, an ingredient network (InN) is constructed, highlighting interconnections among ingredients sourced from different recipes. We delve into both the defining attributes of INDoRI and the communal dimensions of InN. Additionally, we outline the potential applications that can be developed leveraging this dataset. Addressing one of the applications, we demonstrated a research problem on InN with a simple weighted community detection algorithm. Furthermore, we provide a comparative analysis of the results obtained with this algorithm against those generated by two baselines.