Addressing Marketing Bias in Product Recommendations
Mengting Wan∗

Jianmo Ni

Rishabh Misra

Julian McAuley

Airbnb, Inc.
mengting.wan@airbnb.com

UC San Diego
jin018@eng.ucsd.edu

Twitter, Inc.
rmisra@twitter.com

UC San Diego
jmcauley@eng.ucsd.edu

arXiv:1912.01799v1 [cs.IR] 4 Dec 2019

ABSTRACT
Modern collaborative filtering algorithms seek to provide personalized product recommendations by uncovering patterns in consumerproduct interactions. However, these interactions can be biased by
how the product is marketed, for example due to the selection of aparticular human model in a product image. These correlations may
result in the underrepresentation of particular niche markets in theinteraction data; for example, a female user who would potentiallylike motorcycle products may be less likely to interact with them ifthey are promoted using stereotypically ‘male’ images.
In this paper, we first investigate this correlation between users’interaction feedback and products’ marketing images on two realworld e-commerce datasets. We further examine the response of
several standard collaborative filtering algorithms to the distribution of consumer-product market segments in the input interaction
data, revealing that marketing strategy can be a source of bias formodern recommender systems. In order to protect recommendation
performance on underrepresented market segments, we develop
a framework to address this potential marketing bias. Quantitative results demonstrate that the proposed approach significantly
improves the recommendation fairness across different market segments, with a negligible loss (or better) recommendation accuracy.
ACM Reference Format:
Mengting Wan, Jianmo Ni, Rishabh Misra, and Julian McAuley. 2020. Addressing Marketing Bias in Product Recommendations. In The Thirteenth
ACM International Conference on Web Search and Data Mining (WSDM ’20),
February 3–7, 2020, Houston, TX, USA. ACM, New York, NY, USA, 9 pages.
https://doi.org/10.1145/3336191.3371855

1

INTRODUCTION

By connecting users to relevant products across the vast range available on e-commerce platforms, modern recommender systems are
already ubiquitous and critical on both sides of the market, i.e., consumers and product sellers. Among recommendation algorithms
used in practice, many fall under the umbrella of collaborative filtering [13, 17, 20, 25], which collect and generalize users’ preference
patterns from logged consumer-product interactions (e.g. purchases,
ratings). These feedback interactions can be biased by multiple factors, potentially surfacing unfair (or irrelevant) recommendations∗ Work was done while the author was at UC San Diego.

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https://doi.org/10.1145/3336191.3371855

Marketing Bias
Dominating
market segment
Underrepresented market segment

Biased Data

UnfairRecommendations
market loss,
unsatisfactory user experience,social concerns,
etc.

Figure 1: Two illustrative examples on how the same productcan be marketed using different human images (different
body shapes, different genders). These marketing strategies
could affect consumers’ behavior thus resulting in a biased
interaction dataset, which is commonly used as the input formodern recommender systems.
to users or items underrepresented in the input data. Such phenomena have already raised some attention from the recommendersystem community: a handful of types of algorithmic biases havebeen addressed, including selection bias [26], popularity bias [29],and several fairness-aware recommendation algorithms have been
proposed [3, 6]. In this paper, we focus on a relatively underexplored factor—marketing bias—in consumer-product interaction
data, and study how recommendation algorithms respond to its
effect.
We are particularly interested in the human factors, such as
the profile of the human model in a product image, reflected ina product’s marketing strategies, which (as indicated in previous
marketing studies) could possibly affect consumers’ interactions
and satisfaction [5, 11, 12]. A common hypothesis (known as ‘selfcongruence’) is that a consumer may tend to buy a product because
its public impression (in our case a product image), among otheralternatives, is consistent with one’s self-perceptions (user identity)[12]. Based on this assumption, the selection of human models for a
product (as shown in Figure 1, a product can be represented by models with different body shapes or different genders) could influencea consumer’s behavior. For example, a female user may be less likely
to interact with an armband product which is presumably genderneutral but marketed exclusively via ‘male’ images. As with manyother types of bias, this could lead to underrepresentation of someniche market segments in the input data for a recommender system.
Note if undesired patterns are propagated into recommendation results (e.g. even fewer male-represented products are recommended

to the potential female users), utility from both sides of the marketplace could be harmed. That is, product retailers may lose potential
consumers while users may be struggling to find relevant products. As a consequence, serious ethical and social concerns could
be raised as well.
In this work, we seek to understand 1) if such a marketing bias
exists in real-world e-commerce datasets; 2) how common collaborative filtering algorithms interact with these potentially biased
datasets; and 3) how to alleviate such algorithmic bias (if any) and
improve the market fairness of recommendations. We conclude ourcontributions as follows.
• We collect and process two e-commerce datasets from ModClothand Amazon. Then we conduct an observational study to investigate the relationship between interaction feedback and productimages (reflected in the selection of a human model) as well as
user identities. Different types of correlations in varying degrees
can be observed in these two datasets.
• We implement several common collaborative filtering algorithms
and study their responses to the above patterns in the input data.
For most algorithms, we find 1) systematic deviations across different consumer-product segments in terms of rating prediction
error, and 2) notable deviations of the resulting recommendation
outputs from the real interaction data.
• Note that as the marketing bias could be intricately entangledwith users’ intrinsic preferences, our goal in this work is not topursue the absolute parity of recommendations (e.g. keep recommending products represented by human images which were constantly unfavored by a user). Rather, we expect a fair algorithm
is supposed not to worsen the market imbalance in interactions.
We thus propose a fairness-aware framework to address it bycalibrating the parity of prediction errors across different market segments. Quantitative results indicate that our framework
significantly improves recommendation fairness and provides
better accuracy-fairness trade-off against several baselines.

2

RELATED WORKThis work is partially motivated by the well-known ‘self-congruity’
theory in marketing research, which is defined as the match between the product/brand image and the consumer’s true identity
and the perception about oneself [5, 11, 12]. Many previous marketing studies focus on assessing this theory by quantifying andvalidating it through statistical analysis on a small amount purchasetransaction data or the feedback in questionnaires [18, 21, 27, 28].
Following self-congruity theory, products can be advertised in a
way to match their target consumers’ images thus establishingproduct stereotypes [10]. Our work is distinguished with thesestudies from a more computational perspective, by identifying andstudying the potential marketing bias for recommender systems onlarge-scale e-commerce interaction datasets.
Our analysis is related to previous work which examines particular types of biases in real-world interactions and their effects inrecommendation algorithms, including the popularity effect andcatalog coverage [14], the bias regarding the book author gender forbook recommenders [7], and the herding effect in product ratings[31].

ModClothElectronics#review#item#user
time span

99,893
1,020
44,783
2010-2019

1,292,954
9,560
1,157,633
1999-2018bias typebody shapegenderFemale (4,090)
Female&Male (2,466)
Male (3,004)
Female (71,043)
Male (61,350)
N/A (1,025,240)

product image

Small (838)
Small&Large (182)user identity

Large (9,395)
Small (30,140)
N/A (5,248)

Table 1: Basic statistics of the ModCloth and Electronics
datasets.
Another closely related line of work includes developing evaluation metrics and algorithms to address fairness issues in recommendations. ‘Unbiased’ recommender systems with missing-not-atrandom training data are developed by considering the propensityof each item [15, 26]. A fairness-aware tensor-based algorithm is
proposed to address the absolute statistical parity (i.e., items are expected to be presented at the same rate across groups) [32]. Several
fairness metrics and their corresponding algorithms are proposed
for both pointwise prediction frameworks [6, 30] and pairwise
ranking frameworks [3]. Methodologically, these algorithms canbe summarized as reweighting schemes where underrepresented
samples are upweighted [6, 15, 26] or schemes where additional
fairness terms are added to regularize the model [1, 3, 30].
Note that most of the above studies focus on bias and fairness
on one side of the market only (i.e., either user or producer). Ourconcern about marketing bias is that it could affect fairness for bothconsumers and product providers. Without global market fairness in
mind, the imbalance of the consumer-product segment distribution
could be exacerbated through the deployment of recommendation
algorithms. Multi-sided fairness is addressed by Burke et al [3] byconsidering C(onsumer)-fairness and P(rovider)-fairness. Trade-off
between accuracy and fairness in two-sided marketplaces is further
explored and a counterfactual framework is proposed to evaluate
different recommendation policies without extensive A/B tests [22].
However the CP-fairness condition where fairness is protected forboth sides at the same time still remains an open question.

3

DATA COLLECTION AND PREPROCESSING

We introduce two real-world e-commerce datasets collected from awomen’s clothing website ModCloth1 and the Electronics categoryon Amazon.2 These datasets enable us to study the marketing bias
induced by the selection of a human model with respect to body
shape for clothing products, and investigate the effects from the gender of human models for electronics products. Detailed information
about these datasets can be found in Table 1. Datasets in this paper
are available at https://github.com/MengtingWan/marketBias.
Note that our datasets are not perfect, e.g. errors and selection
bias can be introduced via scraping, parsing and processing, control
1 https://www.modcloth.com/
2 https://www.amazon.com/

of several confounding factors including inventory status, (etc.). Our
intention here is neither to make any normative claims regarding
the distributions in the above two applications, nor draw any causal
conclusions. Rather, we simply describe the current state of thesedatasets and study how recommendation algorithms interact withthese data.

3.1

ModCloth

ModCloth is an e-commerce website which sells women’s clothing
and accessories. One unique property of this data is that many
products include two human models with different body shapes (asshown in Figure 1) and measurements of these models. In addition,
users can optionally provide the product sizes they purchased andfit feedback (‘Just Right’, ‘Slightly Larger’, ‘Larger’, ‘Slightly Smaller’or ‘Smaller’) along with their reviews. Therefore we focus on thedimension of human body shape as the source of marketing bias inthis dataset.
Product Image Group (Body Shape). We start with the clothing
products included in an existing public dataset [23], re-scrape theirlanding pages, collect related model size measurements and all
review ratings. We normalize their product sizes as ‘XS’, ‘S’, ‘M’,
‘L’, ‘XL’, ‘1X’, ‘2X’, ‘3X’ and ‘4X’ according to the provided size
charts.3 Products with only one human model wearing a relatively
small size (‘XS’, ‘S’, ‘M’ or ‘L’) are labeled as the ‘Small’ group while
products with two models (an additional model wearing a plus-size:
1X’, ‘2X’, ‘3X’ or ‘4X’) are referred as the ‘Small&Large’ group.
User Identity Group (Body Shape). We then calculate the average size each user purchased and classify users into ‘Small’ and‘Large’ groups based on the same standard as the product body
shape image.
We observe that all products offer the complete spectrum of sizes,
while 70% of these products are interacted with by at least one userfrom the ‘Large’ group and 97% are interacted with by the ‘Small’group. Thus we conclude that most users are able to consume mostproducts at some point within the time frame of our dataset.
Ultimately we collect nearly 100K reviews about 1,020 clothing
products from 44,783 users, where around 90% of users can bematched to the above identity groups.

3.2

Electronics

Electronics is another review dataset collected from the Electronics
category on Amazon with Clothing as an auxiliary category. Thisdataset is built on top of the public Amazon 2018 Dataset [24] andfurther processed to facilitate the research goals in this paper. We
regard the gender as the target marketing bias on this dataset.
Product Image Group (Gender). In the Amazon 2018 Dataset, we
keep all pictures associated with electronic products4 and run human model detection through an industrial body/face detection API
provided by Face++.5 The results include whether any human bodies/faces are included in the pictures, as well as gender predictionsof these detected models. We only keep products where humanmodels are detected in their associated pictures and treat them as

Term/Symbol

Description

product image

the public impression of a product; attributesof the human models included in the productpictures are used in this work, e.g. body shape,
genderthe perception of oneself; we use the same
dimension of attribute as in product image
user identity group, product image group,e.g. female/malethe number of possible user identity groups
and product image groups
predicted user u’s preference score on producti, user u’s rating score on product i, prediction
error eu,i = su,i − ru,ithe user set with the same identity m, the item
set with the same product image n
the market defined for users with the same
identity m on products with the same type ofimage nthe complete interaction datainteractions within the market segment (m, n),ÐÐ
Dm = n Dm,n , Dn = m Dm,nuser identity
m, n
M, N
su,i , ru,i , eu,iUm , In
market segment(m, n)
D = {ru,i |u, i}Dm,n , Dm , Dn

Table 2: Important terms and notation.
three types of product gender image based on the selection of thesehuman models: ‘Female’ (only female models are included), ‘Male’(only male models are included) and ‘Female & Male’ (both female
and male models are detected, not necessarily in the same picture).
We then involve 3 human labelers to conduct validations on thisdataset, where label conflicts are resolved by majority voting. 3,000
randomly sampled pictures are manually labeled regarding 1) if they
notably include human models; 2) the gender image from ‘Female
Exclusive’, ‘Male Exclusive’ or ‘Both Female & Male’ (if multiple
models are included in a single picture). We evaluate the humanmodel detection results from the API based on these labels and finda high precision (96%) regarding the human model detection but
a relatively low recall (53%). Note in our setting we are happy todiscard ambiguous cases (sacrifice some recall) for the sake of highprecision. We later randomly sample 100 products and manually
decide if these products preserve any gender constraints based on
their descriptions. Although 4 out of 100 products exhibit gender
implications,6 we don’t find any strict constraints which prevent theunfavorable user identity group from consuming these products.
User Identity Group (Gender). Unfortunately, gender identitiesof Amazon users are not directly accessible. We thus leverage users’interactions with Clothing products in the Amazon 2018 Dataset toaccess their gender identities, where most products are explicitlyclassified into Women’s Clothing or Men’s Clothing. As shown in
the figure below, we find a clear bimodal distribution of purchasefrequency towards gender-specific clothing products.

3 e.g. https://www.modcloth.com/size-guide.html
4 All products attached to the ‘Men’ or ‘Women’ categories are removed.
5 https://www.faceplusplus.com/

6 e.g. https://www.amazon.com/gp/product/B00HX19EDI

Hist. of Users' Gender-Specific Purchases

ModClothχ26p-value #reviews

all

158.7

<0.001

91,526 581.8<0.001

174,1242<=20142015
2016>=2017

0.5
66.7
70.8
29.0

0.466
<0.001<0.001<0.001

25,383 151.0
20,241 172.7
21,239 96.4
24,663 120.8<0.001<0.001<0.001<0.001

49,69946,891
43,90733,627

0.0
0.2
0.4
0.6
0.8
1.0
Proportion of Men's Clothing Purchases

We discard ‘ambiguous’ users whose men’s (or women’s) clothingpurchase frequencies fall into 40%-60%, and identify the remaining
users as ‘Female’ (69%) or ‘Male’ (31%). Finally 11% of total users in
the Electronics category can be matched to these identities and 53%of them are identified as ‘Female’.
After removing products without any human models, we are still
able to obtain a large-scale dataset containing around 1.3M ratingscores across 9,560 electronics products from 1.1M users. Note that
although the inferred product gender image and user identity are
not as precise as in ModCloth, this dataset is dramatically different
from ModCloth regarding its scale and sparsity. In contrast to therelationship between a user’s interactions with clothing products
and a dimension of human body shape, we speculate that gender is
intuitively less relevant to the intrinsic qualities of most productsin Electronics; thus its effects on users’ interactions are possibly
more likely to come from marketing bias.

STATISTICAL ANALYSIS

We split a consumer’s product preference into two dimensions: 1)
the user’s preference in terms of willingness to consume (purchase)a product; and 2) the user’s satisfaction feedback (e.g. ratings) on
the consuming experience. We then conduct observational studies
on the ModCloth and Electronics datasets to address marketingbias across the above two dimensions.
• We first investigate if there is a bias introduced by a particular marketing effect in a consumer’s product selection process.
Specifically, we examine if a correlation exists between productimage and user identity in terms of interaction frequency in ourdatasets.
• Then we study consumer satisfaction regarding the purchased
products as a function of product image, user identity, and theirsecond-order interactions. These consumer feedback signals include rating scores on ModCloth and Electronics, as well as thebinarized fit feedback (i.e., if the clothing product fits the user)
on ModCloth.
Important terms and notation throughout the paper are includedin Table 2.

4.1

p-value #reviews

4

0

4

Electronics
χ2

Product Selection vs. Marketing Bias

Because of the constraint of conducting real-world experimentswith random assignments, we instead address marketing bias inproduct selection by analyzing the association between productimage and user identity in observed data with respect to interaction
frequency. Our null hypothesis is that product image and user
identity are statistically independent. Given this assumption, we
expect to see lower deviations of their observed frequencies and theTable 3: Results from χ 2 test of the two-way contingency tables on ModCloth and Electronics.

User Identity
Large

Product Image

Small

Small
Small&Large

31,800 (+754.98)
41,361 (-754.98)

7,038 (-754.98)
11,327 (+754.98)

38,83852,688All

73,161

18,36591,526All(a) ModClothUser Identity
Male

All

FemaleFemale&Male
Male

34,259 (+1,472.89) 31,587 (-1,472.89)
26,478 (+880.88) 24,930 (-880.88)
25,963 (-2,353.77) 30,907 (+2,353.77)

65,846
51,40856,870All

86,700174,124

Product Image Female

87,424(b) Electronics

Table 4: Contingency tables of the frequency distribution of
product images and user identities on ModCloth and Electronics. Deviations (fm,n − Efm,n ) from the expected frequency values are provided in parentheses.
marginally expected values. Therefore the following Pearson’s ChiSquared Test Statistic can be used to test the association between
these two variables in terms of frequency [8]:
χ2 =

Õ (fm,n − Efm,n )2
m,n

,

(1)

Efm,n

where m and n represent a user identity group and a product imagegroup respectively, fm,n is the observed numberof interactionsinÍÍ
the market segment (m, n) and Efm,n =(fm ′,n )( n ′ fm,n ′ )m ′Í
( m ′, n ′ fm ′, n ′ )rep-resents its expectation. The null hypothesis will be rejected (i.e., theassociation between two variables exists in terms of frequency) if
an extremely large χ 2 is obtained (i.e., small p-value).
To further separate the potential marketing bias from trending
effects, we conduct association tests on the complete interaction
data as well as interactions within different time spans. Test results
are included in Table 3, where we find all p-values are smaller
than 0.001 except for the test on interaction data before 2014 on
ModCloth. These results may imply the existence of the association
between product image and user identity in consumers’ productselections.
In Table 4, we provide contingency tables of the frequency distribution of different market segments and their deviations from

productuser
user×product

171.9
46.3
30.7

<0.001 293.1<0.001 402.4<0.001
0.0<0.001<0.001
0.997

62.6
3.5
0.9

<0.001
0.061
0.404

Table 5: Results from two-way analysis of variance (ANOVA)on ModCloth and Electronics.
expected values (fm,n − Efm,n ). We observe generally more interactions than expected on the consumer-product segments whereusers’ identities match the product images (‘self-congruity’), while
several market segments are underrepresented in the data. For example, (‘Large’ user, ‘Small’ product) on ModCloth and (‘Female’user, ‘Male’ product) on Electronics have smaller market sizes
compared with other market segments.

Consumer Satisfaction vs. Marketing Bias

Next we investigate consumer satisfaction as a function of productimage and user identity through a standard statistical technique:two-way analysis of variance (ANOVA) [16]. We use rating scores
to represent users’ satisfaction regarding the overall quality of theirconsuming experience on both ModCloth and Electronics. For
ModCloth, we also study consumer satisfactions with respect totheir fit feedback (where ‘Just Right’ is regarded as positive while
all others are regarded as negative). The two-way ANOVA model
can be formulated asconsumer satisfaction ∼ product + user + product × user,
where the null hypotheses of our tests include(a) the average consumer satisfaction is equal across different product image groups;(b) the average consumer satisfaction is equal across different consumer identity groups;(c) there is no interaction effect between product groups and consumer groups with respect to satisfaction.
Given these assumptions, we may expect a lower variance of average satisfactions across different groups (between-group variation)compared with the summation of satisfaction variations within eachgroup (within-group variation). Therefore, the standard F-statistic,
defined as the between-group variation divided by the within-groupvariation [16], can be applied to evaluate the correlations.
Results from statistical tests are included in Table 5. The heatmaps
of sample means within market segments and their 95% confidence
intervals are provided in Figure 2. We observe that users’ ratingscores are significantly different across market segments on ModCloth. For example ‘Large’ users provide lower ratings on ‘Small’
products (Figure 2a). Although users’ fit feedback differs acrossproduct groups and user groups (hypothesis (a) and (b) are rejected
in Table 5), their association regarding fit feedback is negligible(results for ‘user×product’ in Table 5). According to Figure 2b, wefind clothing products in the ModCloth dataset generally fit betteron ‘Small’ users, and those products represented by human models
with different body shapes (‘Small&Large’) tend to obtain better
fit feedback. Although the ‘self-congruity’ pattern is significant

4.06
4.24
(±0.01) (±0.01)
Small
Small&Large
product image

0.66
0.72
(±0.01) (±0.01)
0.58(±0.01)0.64(±0.0)

4.24
4.24
4.17
(±0.01) (±0.01) (±0.02)
4.24
4.22
4.16
(±0.01) (±0.02) (±0.01)
Female Female&Maleproduct image

Small
Small&Large
product image(a) ModCloth (Rt.) (b) ModCloth (Fit)Male(c) Electronics (Rating)Figure 2: Heatmaps of sample means within market segments regarding (a) rating scores on ModCloth, (b) fit feedback on ModCloth and (c) rating scores on Electronics.
in the product selection process on Electronics (see Table 3, Table 4b), the interaction between product ‘gender’ and user genderis insignificant with respect to users’ rating scores (user×productin Table 5).

4.3
4.2

4.18
4.26
(±0.03) (±0.02)

user identity
Male
FemaleRatingFit
RatingF-stat p-value F-stat p-value F-stat p-valueuser identity
Large
Small

Electronics
user identity
Large
Small

ModClothSummary of Observations

We summarize insights obtained from the above statistical analysis
as follows:• The association between product image and user identity is consistently significant in terms of frequency distribution, implying the existence of marketing bias in the collected interaction
datasets. The ‘self-congruity’ pattern is also observable, i.e., consumers may generally tend to interact with products with similar
impressions as their identities. Such an association notably causes
underrepresentation of certain market segments.
• The relationship between consumer satisfaction and marketing
factors is rather complicated. We observe rating disparities acrossproduct groups and user groups, while the existence of theirinteraction effect depends on the type of product and the type ofsatisfaction measure. We find a similar ‘self-congruity’ pattern
for rating scores on ModCloth while the ‘user×product’ term
remains insignificant in the other two testing scenarios.

5

MARKET-FAIRNESS OF RECOMMENDERSYSTEMS

From the above analysis, we have confirmed that our interaction
data is correlated to (and possibly affected by) marketing strategies
used by product retailers (i.e., selections of human models). Our
next step is to study if (and how) this marketing bias is propagatedby algorithms from input data to recommendation results.
Problem Setting. In this study, we focus on recommendation algorithms trained on explicit feedback (i.e., rating scores). The primarypredictive task is formulated as a rating prediction problem: ratingscores (ru,i ) are assumed to reflect users’ preferences over products,
and algorithms are trained to generate users’ product preference
scores (su,i ) which approximate these ratings.
Unlike previous studies [3, 4, 30, 32] which focus on evaluating and protecting the fairness of a single side (user or product)of recommender systems, in the context of marketing bias, weare particularly interested in the global market fairness of the recommendations, i.e., user-fairness and product-fairness need to beprotected at the same time. Specifically we describe the market
fairness in the explicit feedback setting along two dimensions.

(a) Averaged errors of rating predictions from a recommendation
algorithm across different consumer-product market segments
are expected to be equal.
(b) The distribution of market segments in terms of frequency
within recommended interactions are expected to be consistent with the distribution within the real interaction data.
Rating Prediction Fairness. We notice that the first market fairness description is indeed consistent with the null hypothesis ofa one-way ANOVA test about the association between prediction
errors (eu,i = su,i − ru,i ) and market segments ((m, n)). That is,with the assumption that average prediction errors from a fair algorithm are supposed to be irrelevant to market segments, we expect
to observe a lower variation of average errors across market segments (bewteen-segment variation) compared to the error variations
within each segment (within-segment variation). Specifically thesevariations can be defined as2
1 Õ|Dm,n | ēm,n, · − ē
|D| m,n2
1 Õ Õeu,i − ēm,n, ·within-segment var.: U (market) =|D| m,nbetween-segment var.: V (market) =u ∈Um ,i ∈In

where ēm,n, · denotes the sample mean of prediction errors within
the market segment (m, n); |Dm,n | represents the number of interactions included in a consumer-product segment (m, n); |D| denotes
the total sample size.
To ensure a tractable distribution for significance testing, theabove two terms are corrected by their degrees-of-freedom and thefollowing F-statistic can thus be calculated:
F (market) =V (market) / (M × N − 1).
U (market) / (|D| − M × N )|{z}Then we obtain a fairness evaluation metric to evaluate a global
parity of prediction errors across different consumer-product market segments, where lower F indicates better rating prediction
fairness.
Product Ranking Fairness. We further investigate the fairnessof the product ranking performance from recommendation algorithms. For each user, we rank all products based on the predicted
preference scores su,i and regard the top-ranked K items as recommended products. By gathering users and the recommended
products, we are able to obtain the frequency distribution of market
segments within these predicted interactions pm,n ∼ P. We regard
the frequency distribution of market segments in the real interactions qm,n ∼ Q as the reference distribution, and evaluate thedeviation of P from Q using the following KL-divergence [19]:Õm,npm,n log




pm,n
.
qm,n

A FAIRNESS-AWARE FRAMEWORK

A popular choice to model the preference score su,i is throughmatrix factorization [17]su,i = b0 + bi + bu + γi , γu .

(5)

In Eq. (5), b0 is the global intercept, bi and bu are item-specific anduser-specific offsets, γi and γu are d-dimensional embeddings tocapture items’ latent properties and users’ latent preferences onthese dimensions.
Error Correlation Loss. Following previous work using the regularizing schemes [1, 3, 30], we propose a fairness-aware framework
by considering an error correlation loss to regularize systematic
error biases on the market:ÕL∗ =(su,i − ru,i )2 + α Lcorr . ,(6)where Lcorr . is an additional term to regularize the correlation
between prediction errors eu,i and the distribution of market segments (m, n). α is a hyperparameter to control the trade-off between
prediction accuracy and this correlation penalty term.
In practice, we consider the following form by relaxing the evaluation metric Eq. (2):error parity on user identity

error parity on market segmentsz}|{z }| {(u.)(p.)
V
V (market)
V
Lcorr . = κ (u.)+κ (market)+κ (p.)
,
(p.)
(u.)
U
U (market)
U
|{z}

(7)

error parity on product image(2)

correction of degree of freedom

D KL (P |Q) =6

A common optimization criterion for model-based collaborative
filtering algorithms in the explicit feedback setting is based on MSE,
i.e., minimizing the following loss functionÕL=(su,i − ru,i )2 .
(4)

(3)

We use this metric to evaluate the product ranking fairness. Lower
D KL indicates better fairness.

where V (u.) , U (u.) , V (p.) , U (p.) can be implemented by merging market segments within the same type of user identity groups or product image groups. Note that the three error parity terms in Eq. (7)can be regarded as simplified implementations of the fairness metricin Eq. (2). κ (u.) , κ (p.) , κ (market) ∈ {0, 1}3 are binary hyperparameters to instantiate different forms of correlation loss. For example,
a selection of (κ (u.) , κ (p.) , κ (market) ) = (1, 0, 0) represents that weonly penalize the correlation between prediction errors and user
identity groups.

7

EXPERIMENTS

We conduct experiments on the collected ModCloth and Amazon
datasets to evaluate the recommendation performance and themarket fairness as described in Section 5.
Baselines. The following standard algorithms are considered:• itemCF, an item-based collaborative filtering algorithm [20, 25];
• userCF, a user-centric collaborative filtering method [13];• MF, the matrix factorization method [17], where the value of thepreference prediction su,i is unbounded;• PoissonMF, a hierarchical Bayesian framework where the preference factorization is linked to the rating score through a Poisson
distribution, so that the preference score su,i is bounded as apositive value [9].

L=κ (u.) Õκ (p.) Õκ (market) ÕMSEm +
MSEn +
MSEm,n .
M m
N n
MN m,nBy comparing it with other baselines, we study if the marketing bias can be alleviated by simply increasing the weights ofunderrepresented segments in the training data.
For all above methods, we primarily evaluate their rating prediction
accuracy through MSE and MAE, and rating prediction fairness
in terms of the F-statistic (Eq. (2)). We also evaluate their recommendation accuracy through AUC and NDCG, and the productranking fairness in terms of KL-divergence (Eq. (3)).
Experimental Details. We use the following rules to split interactions into train/validation/test sets: for users with at least two
reviews, their most recent ratings are regarded as a test set; forusers with at least three reviews, their second-to-last ratings areused for validation; the remaining interactions are used for training.
We apply the same analysis on both training and test sets as in
Section 4, and find similar patterns except that fewer female users(40%) are included in the test set of Electronics.7
We use the ADAM optimizer [2] with a learning rate of 0.001,
a batch size of 512 and a fixed dimensionality of the latent embeddings in all model-based methods (d = 10). An ℓ2 regularizor
is applied on all model-based methods, where λ is selected from{0.01, 0.1, 1.0, 10}. The accuracy-fairness trade-off α is chosen from
{0.5, 1.0, 5.0, 10.0}. All hyperparameters are selected based on therecommendation accuracy8 on the validation set. For fairness-aware
methods, we search hyperparameters κ = (κ (u.) , κ (p.) , κ (market) )from {(1, 0, 0), (0, 1, 0), (0, 0, 1), (1, 1, 0), (1, 1, 1)}. For each κ, we first
decide all other hyperparameters based on their recommendation
accuracy, then select κ which yields the fairest recommendation
results on the validation set. For each user, the top-10 ranked products are regarded as recommended items. Reviews in the test set
where rating scores are larger than 3 are considered as reference
7 85% female users (vs. 73% male users) have only one review in our entire dataset.
8 MSE for rating prediction accurcy and NDCG for product ranking.

(M,F&M)
(F,M)

(S,S)
(S,S&L)

(M,F)
(F,F)

0.0

F
M

Po

is
s

on

er

M

CF

F

−0.1

us

F

F

(a) ModCloth

M

nM

F

so

rC
is
Po

us
e

ite

m

CF

−0.2

(F,F&M)
(M,M)

0.1

CF

0.0

m

diff. between MSEs

0.2

ite

(L,S)
(L,S&L)
diff. between MSEs

By studying the recommendation outputs from these methods, weevaluate how standard collaborative filtering algorithms respondto the marketing bias in the input data.
We implement our proposed framework (MF (corr.error)), where
su,i is factorized using matrix factorization. By comparing its performance with the above methods (especially MF), we evaluate
if the rating prediction and the product ranking fairness can be
improved without losing much accuracy by adding the proposedcorrelation loss. Besides, we consider another two fairness-aware
alternatives:• MF (corr.value), a method similar to MF (corr.error) exceptthat Lcorr . is implemented as the correlation between the predicted rating values su,i and the market segments. By comparing
MF (corr.error) with it, we evaluate the effectiveness of controlling the parity of prediction errors instead of the absolute
statistical parity of prediction values.
• MF (reweighted), a method where the loss function is reweighted
based on the sizes of market segments in the training data. We
also consider the following generic form of the loss function:(b) Electronics

Figure 3: Differences between the out-segment MSEs and thein-segment MSEs. Market segments are sorted based on theirmarket sizes in the training data.
interactions for the ranking task. All results are reported on thetest set.

7.1

How does a standard collaborative filtering
algorithm respond to biased input data?

We report the above mentioned rating prediction and product ranking metrics on ModCloth and Electronics, regarding both accuracy
and fairness, in Table 6. We first investigate standard recommendation methods without any explicit fairness controls (i.e., itemCF,
userCF, PoissonMF and MF). We observe that most methods
yield biased prediction results on both datasets according to theF-statistic-based significance test. Although we find seemingly fair
prediction errors from userCF, it actually produces a much larger
MSE (as well as worse product ranking results) compared to othermethods.
We further calculate the differences between the out-segment
MSEs and the in-segment MSEs for these algorithms. Given a market segment (m, n), we havediff m,n = MSEu<Um ori<In − MSEu ∈Um andi ∈In .

(8)

diff m,n > 0 indicates, for an algorithm, the market segment (m, n)
is more predictable (smaller MSE) than the interactions outside
it. These differences are displayed in Figure 3, where the market
segments are sorted based on their training sizes. We observe an
overall trend that all algorithms generally tend to favor the dominating market segments (e.g. ‘Small’ users on ‘Small&Large’ products
in ModCloth) in varying degrees. We find the correlation between
the predictibility and the market segment size is more prominent
on ModCloth but rather complicated on Electronics. However, bycross matching Figure 3 and the contingency table Table 4b, wefind that the trend correlates to the deviations of the real market
size and the expected market size: the consumer-product segments(‘Female’, ‘Male’), (‘Male’, ‘Female’) and (‘Male’, ‘Female&Male’) are
underrepresented based on this difference (fm,n − Efm,n < 0), also
generally unfavored by the recommendation algorithms.
We display the distributions of market segments within positive
interactions where rating scores are larger than 3 and the recommended top-10 products from these algorithms in Figure 4. Compared with the distributions in real interactions (the ‘data’ columns
in Figure 4), we can observe the deviations of recommendation
results from most algorithms, particularly itemCF and userCF on

8
6

userCF

4

Can recommendation fairness be improved
by applying the correlation loss?

In Table 6, we further compare the results from fairness-aware
algorithms in group (b) to the standard algorithms in group (a),particularly MF. To better visualize the trade-off between recommendation accuracy and market fairness, we present scatter plotsof an accuracy metric and a fairness metric on both datasets in
Figure 5. We notice the proposed method with error correlation
loss MF (corr.error) generally provides better rating and ranking
fairness (lower F-statistic and KL-divergence) than standard MF,
without trading-off much recommendation accuracy. An interestingfinding is the combination selection κ on the validation set is consistent with our analysis in Table 5: the complete correlation loss
(κ = (1, 1, 1)) is selected for ModCloth and the addition of productand user correlation (κ = (1, 1, 0)) is selected for Electronics.
We find the reweighting scheme also benefits the fairness metrics, particularly in the product ranking setting. One surprisingfinding is that by applying the error correlation loss, a significant
performance gain in terms of product ranking accuracy (AUC and
NDCG) can be obtained on Electronics. A possible reason could bethat Electronics is an extremely sparse dataset where algorithms
like MF may struggle to converge to an ideal local optimum. The
fairness-aware correlation loss, however, could help regularize thetraining process.

8

CONCLUSIONS AND FUTURE WORK

We conclude our work and summarize our findings as follows:• We investigated a potential source of bias—marketing bias—in
the form of the association between interaction feedback, productimage and user identity, on two real-world e-commerce datasets.
Through observational studies, the inter-correlations between
these factors can be confirmed and the ‘self-congruity’ patterns
are noticeable in the product selection process, which eventually
results in the underrepresentation of some market segments.
• We focused on market fairness and investigated how standard
collaborative filtering algorithms react to this biased input data.
We found such a bias can be propagated to the recommendation
outcomes in varying degrees.

3

1.25

1.50
MSE

1.75

1

2.00

(a) ModCloth (Rating)MF (reweighted)

0.6

userCF
0.2
MF (reweighted)
MF
0.0
PoissonMF MF (corr.error)
0.10

0.15
NDCG

0.20

(c) ModCloth (Ranking)

1.5

2.0
MSE

2.5

0.020

itemCF
MF (corr.value)

0.4

MF (corr.error) userCF(b) Electronics (Rating)

KL-divergence

7.2

PoissonMFMF

2

MF (corr.error)0
1.00

KL-divergence

ModCloth. However, systematic patterns about how these deviations correlate to the sizes of different market segments in the
training data are not observed.

MF (corr.value)

4

itemCF(b) Electronics

Figure 4: Distribution of market segments within test data
(positive interactions only) and within recommendations.
Market segments are sorted based on their sizes in training
data.

itemCF

5

MF (reweighted)

2(a) ModCloth

6

PoissonMF
MF (corr.value)MF
F-stat10

F-stat

)

F)

,F
(M

(F
,

)

)

M

,M

F&

(M

)

M

M

)

0.10

(F
,

&
,S
(S

0.15

(F
,

L)

)
,S
(S

&
,S
(L

(L

L)

0.0

data
itemCF
userCF
PoissonMF
MF

0.20

,F
&

0.2

0.25

(M

dist. of market segments

0.4

,S
)

dist. of market segments

0.6

0.015
0.010

PoissonMF
MF
MF (corr.value)
userCF itemCF

0.005

MF (corr.error)
MF (reweighted)

0.000
0.08

0.10
NDCG

0.12

(d) Electronics (Ranking)

Figure 5: Scatter plots for accuracy-fairness trade-off fromdifferent algorithms. Shaded arrows indicate the most ideal
direction: higher accuracy, better fairness.
• We developed an error correlation framework, which explicitly
calibrates the equity of prediction errors across different market
segments. Experimental results demonstrate that by applying
this correlation loss, a superior accuracy-fairness trade-off canbe achieved.
This work is a first step to approach the potential marketing bias
in machine learning systems. We also wish to address several limitations of our data and methods, and to provide potential research
directions.
• Data. We study marketing bias by formulating it as the relationship between the human model images of products and user identities. Multiple marketing factors (e.g. product descriptions, social
media advertisement contents) can also be considered. Binary
gender identities are inferred in our Electronics dataset, which
is limited to represent user identities that are not exclusively
masculine or feminine, e.g. users who don’t always purchaseproducts corresponding to their own identities, or those who
identify themselves outside the binary definition.
• Analysis. We collect ModCloth and Electronics as logged interactions where many confounders (inventory status, the observability of each product, potential biases introduced in the
scraping and preprocessing stage, etc.) exist and are difficult tobe disentangled. Although the inter-correlation between productimage and user identity is observed in these datasets, we cannot
draw any causal conclusions without controlling some notableconfounding factors. Therefore another direction to validate (ormore fundamentally address) this marketing bias is to conduct

ModClothElectronics

MethodMSE

Rating Prediction
MAE F-stat p-value

Product Ranking
AUC NDCG
KL(a)

itemCF
userCF
PoissonMF
MF

1.398
1.880
1.168
1.176

0.841
0.946
0.859
0.859

2.568
3.889
9.600
9.805

0.053 0.601
0.009 0.504<0.001 0.638<0.001 0.817

0.121 0.557 1.529
0.123 0.303 2.487
0.151 0.001 1.628
0.179 0.015 1.590

0.966
0.980
1.035
1.025

5.099
1.501
4.112
3.447

<0.001 0.619
0.186 0.503
0.001 0.565
0.004 0.591

0.098 0.009
0.087 0.009
0.085 0.014
0.091 0.012(b)

MF (reweighted) 1.290
MF (corr.value)
1.208
MF (corr.error)
1.204

0.872
0.875
0.873

8.402
9.887
1.667

<0.001 0.852<0.001 0.549
0.172 0.818

0.183 0.012 1.615
0.123 0.484 1.617
0.179 0.003 1.543

1.017
1.043
1.011

2.769
4.543
1.896

0.017 0.594<0.001 0.502
0.091 0.766

0.092 0.001
0.086 0.012
0.122 0.002

MSE

Rating Prediction
MAE F-stat p-value

Product Ranking
AUC NDCG
KL

Table 6: Recommendation results on ModCloth and Electronics. For rating predictions, MSE,MAE are used to evaluate the prediction accuracy while the F-statsitic (Eq. (2)) is used to evaluate the prediction fairness and its associated p-value is provided;for product rankings, AUC and NDCG are used to evaluate the recommendation accuracy while the KL-divergence (Eq. (3)) is
used to evaluate the recommendation fairness. The most accurate and the fairest results are underlined.

user-centric randomized experiments or natural experiments.
In this way, causal conclusions and insights can be provided to
product sellers and recommender system practitioners.
• Algorithms. Although we only focus on algorithms trained on
explicit feedback, it is relatively intuitive to extend the proposederror correlation framework to other pointwise recommendation
algorithms. Another direction is to address the marketing bias
in pairwise ranking recommendation algorithms, where market
fairness metrics and debiasing methods can be further exploredto accommodate real-world scenarios.

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