@conference{Elsweiler2017,
title = {Exploiting Food Choice Biases for Healthier Recipe Recommendation},
author = {David Elsweiler and Christoph Trattner and Morgan Harvey},
url = {https://www.christophtrattner.info/pubs/SIGIR2017.pdf},
year = {2017},
date = {2017-08-01},
organization = {ACM SIGIR Conference},
abstract = {By incorporating healthiness into the food recommendation / ranking
process we have the potential to improve the eating habits of a
growing number of people who use the Internet as a source of food
inspiration. In this paper, using insights gained from various data
sources, we explore the feasibility of substituting meals that would
typically be recommended to users with similar, healthier dishes.
First, by analysing a recipe collection sourced from Allrecipes.com,
we quantify the potential for nding replacement recipes, which are
comparable but have dierent nutritional characteristics and are
nevertheless highly rated by users. Building on this, we present two
controlled user studies (n=107, n=111) investigating how people
perceive and select recipes. We show participants are unable to
reliably identify which recipe contains most fat due to their answers
being biased by lack of information, misleading cues and limited
nutritional knowledge on their part. By applying machine learning
techniques to predict the preferred recipes, good performance can
be achieved using low-level image features and recipe meta-data as
predictors. Despite not being able to consciously determine which
of two recipes contains most fat, on average, participants select
the recipe with the most fat as their preference. The importance of
image features reveals that recipe choices are often visually driven.
A nal user study (n=138) investigates to what extent the predictive
models can be used to select recipe replacements such that users
can be “nudged” towards choosing healthier recipes. Our ndings
have important implications for online food systems.},
note = {Pre SFI},
keywords = {Behavioural change, Food RecSys, Human decision making, Information behaviour},
pubstate = {published},
tppubtype = {conference}
}