=Paper=
{{Paper
|id=Vol-2439/6-paginated
|storemode=property
|title=An Evaluation of Recommendation Algorithms for Online Recipe Portals
|pdfUrl=https://ceur-ws.org/Vol-2439/6-paginated.pdf
|volume=Vol-2439
|authors=Christoph Trattner,David Elsweiler
|dblpUrl=https://dblp.org/rec/conf/recsys/TrattnerE19
}}
==An Evaluation of Recommendation Algorithms for Online Recipe Portals==
https://ceur-ws.org/Vol-2439/6-paginated.pdf
An Evaluation of Recommendation Algorithms for Online
Recipe Portals
Christoph Trattner David Elsweiler
University of Bergen University of Regensburg
Norway Germany
christoph.trattner@uib.no david.elsweiler@ur.de
ABSTRACT for example, building nutritional content into the recommendation
Better models of food preferences are required to realise the oft process [15, 19, 34] or by recommending meal plans, which tailor
touted potential of food recommenders to aid with the obesity crisis. recommendations to users’ nutritional needs over time [6].
Many of the food recommender evaluations in the literature have Providing healthful food recommendations, using any of the
been performed with small convenience samples, which limits our suggested strategies necessitates, however, that we can accurately
conidence in the generalisability of the results. In this work we test model and predict the food individual users would actually like to
a range of collaborative iltering (CF) and content-based (CB) re- eat. We have yet limited understanding as to which recommender
commenders on a large dataset crawled from the web consisting of algorithms work best [33] and the studies that have been performed
naturalistic user interaction data over a 15 year period. The results typically focus on one approach in isolation (e.g. recipe ingredients
reveal strengths and limitations of diferent approaches. While CF [11] or properties of the associated image [14]). Moreover, past
approaches consistently outperform CB approaches when testing work has tended to employ datasets derived from small scale user
on the complete dataset, our experiments show that to improve on studies [11, 19] limiting our conidence in the generalisability of the
CF methods require a large number of users (> 637 when sampling results. In this work, we test a number of competitive collaborative
randomly). Moreover the results show diferent facets of recipe con- iltering (CF) and content-based (CB) recommenders on a large
tent to ofer utility. In particular one of the strongest content related scale naturalistic dataset similar to those that have been studied
features was a measure of health derived from guidelines from the for cultural [24, 40] or epidemiological [37] reasons using data
UK Food Safety Agency. This inding underlines the challenges we science methods. We formulate the problem as is typically done
face as a community to develop recommender algorithms, which in recommendation experiments using past feedback from a given
improve the healthfulness of the food people choose to eat. user to predict future interactions by that same user [26]. The aim
being not only to compare and contrast diferent models, but also to
KEYWORDS examine the utility of diferent facets of content - which are diverse
in the case of online recipes - and establish how these inluence the
Online recipes; recommender systems
recommendation performance. The main indings include that:
ACM Reference Format:
Christoph Trattner and David Elsweiler. 2019. An Evaluation of Recom-
mendation Algorithms for Online Recipe Portals. In Proceedings of the 4th • CF methods consistently outperform CB methods over the full
International Workshop on Health Recommender Systems co-located with 13th dataset.
ACM Conference on Recommender Systems (HealthRecSys’19) (HealthRecSys • CF requires either a small number of highly active users or over
’19) , 5 pages. six hundred users, selected randomly to achieve competitive
performance.
1 INTRODUCTION • There is a useful signal in the CB facets, which would be useful
in cold-start situations.
Food recommenders (e.g. [11, 15]) and studies of online recipes (e.g.
• One of the most robust content features is the nutritional health-
[24, 40] ) have received increased research attention of late. A key
iness of the recipe as deined by a measure derived from the
motivation for this is often health, with recommender systems being
United Kingdom Food Standards Agency (FSA). This highlights
touted as a means to help people change dietary habits and address
that users are typically consistent in their nutritional preferences
costly societal problems, such as diabetes and obesity [7, 11].
over time and emphasizes the challenges faced to change eating
Diverse studies have been published, ofering insight into the
habits.
contextual factors inluencing recipe preference [28, 40] and the
future popularity of recipes [36], as well as providing an under-
standing of the links between recipe preference and incidence of The remainder of the paper is structured as follows: Sections 3
eating related illness [37]. A further strain of research has attemp- and 4 describe the data basis and experimental setup, respectively.
ted to incorporate health in the food recommendation problem by, Section 5 continues to report the results of two rounds of experi-
ments, the irst of which uses the full dataset and the second em-
ploys a bootstrapping approach to test algorithms on sub-samples
HealthRecSys ’19, September 20, 2019, Copenhagen, Denmark
of the data of various sizes. Section 6 summarises the indings and
© 2019 Copyright for the individual papers remains with the authors. Use permitted
under Creative Commons License Attribution 4.0 International (CC BY 4.0). This sets these in context against the literature, which is reviewed in the
volume is published and copyrighted by its editors.. following section.
24
HealthRecSys ’19, September 20, 2019, Copenhagen, Denmark Tratner et al.
2 RELATED WORK Table 1: Basic statistics of the Internet recipes dataset ob-
tained from Allrecipes.com.
In this section two bodies of related work are reviewed. The irst
focuses on the evaluation of food recommender algorithms. The
second summarises studies of user interaction with online recipe Total published recipes 60,983
portals, which provides insight into human food preference and Recipes containing nutrition information 58,263
the variables inluencing this. Recipes rated 46,713
Ratings 1,032,226
2.1 Food Recommendation Users providing ratings 125,762
Eforts to design automated systems to recommend meals can be
traced to the mid-1980s where case-based planning was employed
[18, 21]. More recent eforts have focused on rating prediction, using interacted with and a growing body of evidence reports correla-
either aspects of recipe content or ratings data using collaborative tions between recipes accessed via search engines, recipes portals
iltering approaches. Freyne et al. [11] showed the recommenda- and social-media and incidence of diet-related illness [1, 3, 29, 37].
tions could be improved by decomposing recipes into individual Moreover, clear weekly and seasonal trends can be observed in
ingredients and building user proiles comprising ingredients users the way users interact with recipes, both in terms of the contained
liked based on ratings for the recipes containing these ingredients. ingredients and the nutritional value of the recipes (fat, proteins,
Harvey et al. extended the approach and improved performance by carbohydrates, and calories) [23, 40]. Other work has reported difer-
creating positive and negative proiles for users and reducing the ent interaction patterns for users with diferent gender [28, 39] and
dimensionality of the matrices [19]. who live in diferent geographical areas within a country [40, 44].
Other CB approaches have employed visual signals. Yang and The number of variables shown to relate to eating habits highlights
colleagues demonstrated that algorithms designed to extrapolate im- just how challenging a problem food recommendation is.
portant visual aspects of food images outperform baseline methods The brief review of literature above has highlighted the increas-
[42, 43]. Elsweiler et al. [8] also show that automatically extrac- ing popularity of food recsys research and that a key motivator is
ted low-level image features, such as brightness, colourfulness and desire to build systems to promote healthy nutrition. Key takeaways
sharpness can be useful for predicting user food preference. from the review are as follows:
A second approach has been to exploit ratings data using col- • While several evaluations have CF and CB baselines, no extensive
laborative iltering (CF) techniques. Freyne and Berkovsky tested comparison of CF and CB approaches in food recsys domain has
a nearest neighbour approach, which ofered poorer performance been published.
than the content approach described above [11]. Ge et al. [15] tested • Moreover, no detailed investigation of diferent aspects of content
a matrix factorization solution that fuses ratings information and that may be useful is available and much of the recipe content
user supplied tags to achieve signiicantly better prediction accur- (recipe description, cooking steps, cooking time etc.) has not been
acy than content-based and standard matrix factorization baselines. evaluated.
Several studies report that the best results are achieved when CF • Finally, the evaluations performed to date have typically been
and CB approaches are combined in hybrid models [11, 14, 19]. performed on small artiicially generated test collections.
A common motivator for food recommendation work has been
to promote healthy nutrition. One approach is to rely on rules de- 3 MATERIALS
rived from domain experts to meet daily energy requirements [13]
To address the identiied gaps in the literature, in this work, we
or focus on the nutritional requirements of speciic groups such
make use of a web crawl of the online platform Allrecipes.com to
as the elderly care [10] or body-builders [38]. Others have tailored
evaluate diverse CF and CB approaches in the recipe recommenda-
recommendations based on the user’s caloriic or other nutritional
tion context.
needs [15, 16, 34], existing nutritional habits [31] or combine re-
The platform was crawled between 20th and 24th of July, 2015.
commendations to meet requirements [6]. Again, approaches have
We retrieved 60,983 recipes published by 25,037 users between the
been published for speciic target groups e.g. diabetics [25].
years 2000 and 2015 through the sitemap that is available in the
robots.txt ile of the website. In this paper we only make use of the
2.2 Studies of Food Behaviour using Online 58,263 recipes where nutrition information was available. The basic
Recipe Portals statistics of this dataset can be found in Table 1.
While not focusing on recommendation, a large body of recent work In addition to the core recipe components ś such as recipe title,
sheds light on food preferences by studying interactions with on- ingredient list, number of servings and instructions ś we also col-
line food portals. Analysing the nutritional content of these portals lected for each recipe the according image, comments provided by
using metrics derived from the World Health Organisation (WHO) users, rating information and nutrition facts1 , such as total energy
and the United Kingdom Food Standards Agency (FSA) has found (kCal), protein (g), carbohydrate (g), sugar (g), salt (g), fat (g) and
recipes to be mainly unhealthy, although healthy recipes can be saturated fat (g) content (measured in 100g per recipe).
found [35]. Overall, people tend to interact with the least healthy
1 Allrecipes.com estimates the nutritional facts for an uploaded recipe by matching
recipes most often [34]. There is, nevertheless, heterogeneity in
the contained ingredients with those in the ESHA research database [9]. The ESHA
the user-base with respect to the nutritional properties of recipes system is used by popular companies such as MCDonald’s and Kellogs.
25
An Evaluation of Recommendation Algorithms for Online Recipe Portals HealthRecSys ’19, September 20, 2019, Copenhagen, Denmark
Allrecipes.com is just one of many online recipe portals. Others • Directions: From the directions block we computed two similarity
popular sites include Food.com, Epicurious.com, Yummly.com and features based again on a LDA topic vector representation of
Cooks.com. We chose Allrecipes.com because, at the time of writ- the text as well as on TFśIDF vector representation. Similarities
ing, it claims to be the world’s largest food-focused social network: were again computed employing the cosine similarity measure
the site has a community of over 40 million users from 24 countries on these vectors.
who annually visit 3 billion recipes [2]. This claim has been corrob- • Ratings: Here we rely on the the number of ratings of a recipe as
orated by services such as eBizMBA, which ranks Allrecipes.com well the average rating. To compute similarities between recipes
as the most popular recipe website [5]. This means that we not on theses indicators we rely again on the inverse Manhatten
only analyze a large scale dataset, but also the most popular recipe distance, i.e. 1 − |metric(r i ) − metric(r j )|.
platform on the Web. • Health: In order to measure healthiness of a recipe we rely on
the following macro nutrient: ‘fat’, ‘saturated fat’, ‘sugar’ and
4 EXPERIMENTAL SETUP ‘salt’ (measured in 100g per recipe). This allows us to measure
We ran a series of experiments evaluating the performance of the healthiness of a recipe according to international standards
6 prominent recommender algorithms on the rating data using as introduced in 2007 by The Food Standard Agency (FSA) [12].
the LibRec2 framework. The algorithms tested are: Random item There are also other standards that can be applied, such as the
ranking (our baseline), Most Popular item ranking (MostPopular), ones provided by the World Health Organization (WHO) [41]
user- and item-based collaborative iltering (denoted as UserKNN or the HEI metric as proposed by the CDC [20]. We employ the
and ItemKNN) [30], Bayesian Personalized Ranking (BPR) [26], standards provided by the FSA, as this is currently most robust
Weighted matrix factorization (WRMF) [22] and Latent Dirichlet method to estimate the healthiness of online recipes. The metric
Allocation (LDA) [17]. was also used in related work [34]. The scale ranges from 4 for
For the content-based approaches we induced in total 20 diferent very healthy recipes to 12 for very unhealthy recipes. Throughout
features, which we used to compute similarities between recipes. the paper we refer to this metric as ‘FSA score’.
Below we briely summarise these features and their corresponding For each of the features described above, we derive a scoring
sets: function that computes as follows:
Í
• Title: For the title feature set, we derived 5 similarity features, sim(i, p)
based on Levenshein distance, Least Common Sub-Sequence p ∈Pu
score(u, i)f eatur e = , (1)
(LCS), Jaro-Winkler distance and bi-gram distance. To obtain a |Pu |
similarity value between two recipes based on these features where Pu is the set of items of a user u, i an arbitrary item, and
we calculate 1 − dist(r i , r j ). Furthermore, we employ LDA topic sim(i, p) is any of the above mentioned similarity metrics between
modelling on the recipe titles using Mallet with Gibbs sampling. item i and p.
The number of topics was set to 100 topics. Hence for each recipe For each feature set we calculate scores based on the linear
we induce a vector of dimension one hundred capturing the topic combination of the similarities3 .
distribution. To calculate similarities between recipes we employ As in previous work [26], we operationalise the experiments
the cosine similarity metric. as a personalized ranking problem (item recommendation). The
• Image: For the image feature set we employed on the one hand aim here is to provide a user with a ranked list of items where the
side image attractiveness measures such as image brightness, ranking has to be inferred from the implicit behavior of the user
sharpness, contract, colorfulness and entropy as well as deep (e.g. recipes rated in the past). Implicit feedback systems, such as
convolutional neural network (CNN) features from a pre-trained those studied in [26] are challenging as only positive observations
VGG-16 model [32]. For each image we derive one embedding are available. The non-observed user-item pairs ś e.g. a user has
vector of dimension 4096 and calculate cosine similarity between not cooked a recipe yet ś are a mixture of real negative feedback
recipes on these vectors. To measure the similarity between two (the user is not interested in cooking the recipe) and missing values
recipes based on the image attractiveness metrics [36] we employ (the user might want to cook the recipe in the future). We use 5-
the Manhatten distance, i.e. 1 − |metric(r i ) − metric(r j )|. fold cross validation as protocol for all the experiments and report
• Ingredients: To calculate similarities between recipes on ingredi- the recommendation performance results employing AUC as a
ent level, we inducted four diferent features. On the one hand performance metric [27].
side the text itself was used and brought to a TFśIDF repres- To reduce data sparsity issues, a well-known issue in collaborat-
entation to calculate cosine similarity between recipes. On the ive iltering-based methods [27], in the irst experiments we apply
other hand side we also chose to employ LDA again to derive a p-core ilter approach [4] using only user proiles with at least
a topic distribution and to calculate cosine similarity between 20 rating interactions4 and recipes that have been rated at least 20
recipes on those vectors. Finally, we employed the normalized times by the users, resulting in a inal dense dataset comprising
ingredient strings, to calculate similarities between recipes using 1273 users, 1031 items and 50,681 interactions. To study the efects
cosine similarity and Jaccard. In the case of cosine we normalized of diferent levels of users on performance we report a second set
the quantities of each ingredient to 100g of a recipe and used the
normalized quantity values as frequency indicator. 3 Parameters were tuned to the optimum using grid search.
4 We transfer all ratings to positive feedback, i.e. any rating is counted as positive
feedback and any none interaction as negative feedback. This makes sense as 95% of
2 http://www.librec.net/
all ratings in the Allrecipes.com dataset are 5-star ratings, see also [36].
26
HealthRecSys ’19, September 20, 2019, Copenhagen, Denmark Tratner et al.
Table 2: Results of the recommender experiment ś collabor- (A) Dense Data Samples (p−core=20)
ative (CF) vs content-based (CB) ś in the dense data sample ● ●
● ● ● ● ●
●
with all users. Best features in each set (CF and CB) are bol- 0.68 ● Algorithm
AUC
●
● BPR
ded. Top-5 (↑) and Bottom-5 (↓) single content features are 0.64
●
CB:All
also marked. 0.60
●
1
5
10
20
30
40
50
60
70
80
90
100
Method Algorithm AUC Number of Users [%]
(B) Sparse Data Samples (no p−core)
BPR .7094 ● ●
WRMF .6881 0.60 ●
Algorithm
●
UserKNN .6962
AUC
0.56 ●
CF
● ● BPR
ItemKNN .6909 0.52
●
CB:All
●
●
MostPopular .6864 0.48 ● ● ●
LDA .6863
1
5
10
20
30
40
50
60
70
80
90
100
Number of Users [%]
Title:Levenstein-Distance .5468 (↑)
Title:Bigram-Distance .5500 (↑)
Figure 1: (A) shows the results in the dense data samples (=
Title:LCS-Distance .5424
p-core iltered) where each user has at least 20 item interac-
Title:LDA-Text-Cosine .5353
tions and each item is at least 20-times interacted with, (B)
Title:Jaro-Winkler-Distance .5324
shows the results in the sparse data samples (=no p-core).
Title:All .5523
Image:Cosine-Embeddings .5322
Image:Colorfulness-Distance .5072 (↓)
Image:Contrast-Distance .5175
Image:Sharpness-Distance .5109
Image:Entropy-Distance .5080 (↓) AUC scores of > .686. This compares to .5883 achieved by the linear
Image:Brightness-Distance .4991 (↓) combination of content features (= CB:All).
CB
Image:All .5425 Examining the performance of diferent aspects of content (title,
Ingredients:Cosine-Text .5547 image, ingredients, direction and health) shows that there is a signal
Ingredients:Cosine-LDA-Text .5653 (↑) in each of these aspects. This is a sign of the consistency, in terms
Ingredients:Jaccard .5502 of the properties of recipes, which individual users tend to rate.
Ingredients:Cosine .5575 The fact that the combined model łAllž does not achieve a high
Ingredients:All .5718 improvement on these signals individually is perhaps an indication
that a linear combination is not the best means to combine these
Directions:Cosine-LDA-Text .5606 (↑) signals. One of the strongest content-based features is the FSA score
Directions:Cosine-Text .5210 (AUC=.5775). Again, this hints at consistency in user preference,
Directions:All .5731 this time in terms of the healthiness of recipes, which individual
Ratings:Number-Distance .4789 (↓) users interact with.
Ratings:Average-Distance .4832 (↓) To complement these initial results and better understand the
Ratings:All .5249 relationship between CF and CB methods and the amount of data
required to achieve strong recommendation performance with these
Health:FSA .5775 (↑)
approaches, we performed the bootstrapping study as described
CB:All .5883 above. The results are presented in Figure 1.
Random .4989 In a irst test, see Figure 1 (A), we sampled only from active
users, that is, we derived a test size of various sizes where users
had rated at least 20 items and the items involved had also achieved
of bootstrapped experiments using smaller dense samples of heavy at least 20 ratings. Taking this dense sample showed that even a
users (using the same criteria as above), and varying collection sizes small number of users can attain stable performance. With only 1%
using standard random sampling, referred to as ‘sparse samples’ in of all users (N=13) the CF technique (BPR) is able to outperform the
the text. These experiments were repeated 100 times each and the content approach. Nevertheless, when users are selected at random
average performance reported. from the dataset and no p-core ilter is applied, see Figure 1 (B) ś
which we argue is a much more realistic setup [4] ś many more
5 RESULTS users are required on average to achieve an equivalent perform-
ance. Whereas the CB approaches achieve a consistent performance
The results of the experiments on the full dataset are shown in
(AUC=> .54) regardless of the number of users studied, half of the
Table 2. The CF methods clearly outperform the content-based
dataset (50%, N=637) is required before the CF methods outperform
approaches. The best performing CF method (BPR) achieved an
the CB approach.
AUC score of .7094 and the remaining CF methods demonstrated
27
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