Can e-Commerce Recommender Systems be More Popular with

Online Shoppers if they are Mood-aware?

Fanjuan Shi

1,2

and Jean-Luc Marini

Shanghai Unicore Technology of IOT Co., Ltd, Shanghai, China

Search’XPR SAS, Clermont-Ferrand, France

Keywords: Mood Recognition, e-Commerce Recommender System, Behavioral Data Mining, User-centric Systems.

Abstract: This paper presents the result of a controlled experiment studying how mood state can affect the usage of e-

commerce recommender system. The authors develop a mood recognition tool to classify online shoppers

into stressed or relaxed mood state unobtrusively. By analyzing their reactions to recommended products

when surfing on an e-commerce website, the authors make two conclusions. Firstly, stress negatively

impacts the usage of recommender system. Secondly, relaxed users are more receptive to recommendations.

These findings suggest that mood recognition tool can help recommender systems find the “right time” to

intervene. And mood-aware recommender systems can enhance marketer-consumer interaction.

1 INTRODUCTION

As the demand for user-centric web service rises,

recognizing users’ mood in real time becomes more

and more important. Researchers have proposed

various methods to detect users’ mood. These

methods include vocal signal analysis (Koolagudi

and Rao, 2012), facial expression analysis (Fridlund,

2014), physiological indicators analysis (Silva et al.,

2009), content-based semantic analysis (Baldoni et

al., 2012), user input analysis (Khan et al., 2013),

and hybrid solutions (Sebe et al., 2006). Mood-

aware systems can not only analyze and identify

users’ mood in real time, but also take such context

into account and propose correspondent service to

users in the “right time”, a moment when users are

more receptive to these services. In practice, mood-

aware systems have been integrated into video

games (Ambinder, 2011), online learning systems

(D’Mello et al., 2008; Mao and Li, 2009),

productivity software (Bailey and Konstan, 2006)

and other interactive computer systems (Kolakowska

et al., 2013).

This paper focuses on applying mood recognition

to e-commerce recommender systems, where few

attention has been paid to users’ real time mood

states. In view of the European consumer privacy

protection regulations, users’ clickstream data was

chosen as the data source for analysis. In Section II,

we briefly review the main research topics and

methods of mood recognition with computer users.

Section III proposes our hypothesis, theoretical

framework and a two-phased experiment approach.

Section IV presents our experiment setting and

result. Section V discusses about how to apply our

findings to e-commerce and online marketing.

Section VI concludes the paper and provides

suggestions for future research.

This paper add value to the research of context-

aware recommender systems in three aspects.

 It proposes a mood recognition tool which can

recognize website users’ mood state.

 It confirms that mood state affects users’

reaction to the recommendations.

 It proves that recommendations of mood-

aware system are more popular with online

consumers.

These findings can help marketers to find new

technics to satisfy online consumers and enhance the

interaction between marketer and consumer.

2 RELATED WORKS

Recognizing users’ mood is not a novel concept. The

discussion about mood recognition is associated

with four major aspects: How to define mood?

Should mood be elicited? Which kinds of behavioral

Shi, F. and Marini, J-L.

Can e-Commerce Recommender Systems be More Popular with Online Shoppers if they are Mood-aware?.

In Proceedings of the 12th International Conference on Web Information Systems and Technologies (WEBIST 2016) - Volume 2, pages 173-180

ISBN: 978-989-758-186-1

173

indicators to choose? How to associate behavioral

indicators with mood?

2.1 Defining Mood

Mood can be referred as a specific emotion or state

of mind (Lane and Terry, 2000). In general,

researchers follow two different tracks to recognize

users’ mood.

In the first track, researchers encourage users to

describe their mood state with their own mood

taxonomy (Lee et al., 2012), and there is no limit on

how many types of mood they can report (Epp et al.,

2011). The advantage of this approach is that it helps

to better understand individual level and complex

mood states. However, aggregating users’ feedbacks

and making general conclusions can be challenging

as taxonomy of mood are not standardized across

users.

In the second track, the taxonomy of mood is

predefined by researchers. Sometimes the taxonomy

is exhaustive. One of the example is to define mood

as “positive, neutral or negative” (Zimmermann et

al., 2006; Khanna and Sasikumar, 2010). In other

cases, researchers are interested in some specific

moods such as sadness, fear, anger, surprise, or

happiness (Lv et al., 2008). Consequently, their

taxonomy of mood is non-exhaustive. As predefined

taxonomy provides a standard to compare and

classify the mood state of different users, it has been

widely used by researchers who want to recognize

and classify mood state of a group of users.

2.2 Eliciting Mood

Depending on the role of researchers in the

experiment, the methods to study mood state can be

classified into two groups.

In the first group, researchers play the role of

observers. There is no stimulus to affect users’ mood

state. During the experiments, users’ mood state are

evaluated either by users themselves (Lee et al.,

2012), or by trained assessors in an unobtrusively

manner (Sottilare and Proctor, 2012). These methods

allow researchers to associate moods with different

kinds of behavioral features.

In the second group, various stimuli are used by

researchers to elicit desired mood state. For

example, requesting users to watch a video (Maehr,

2005; Zimmermann et al., 2006) or listen to a story

(Lv et al., 2008) can provoke mood. Giving users

challenging tasks (Vizer et al., 2009) or interrupting

them when they are working (Epp et al., 2011) can

cause stress. These methods help researchers verify

whether certain behavioral features are universal

across different users under a given mood state.

2.3 Selecting Behavioral Indicators

As mood might affect behavior, it is necessary to

record users’ behaviors for further analysis. Users’

behavioral data is represented by a multi-dimension

vector. Each dimension stands for a type of behavior

indicator defined by the researchers. In the

computer-assisted experiments, keystroke and

mouse activities are commonly used by researchers

as behavioral indicators (Zimmermann et al., 2003;

Salmeron-Majadas et al., 2014).

Keystroke behavior indicators are associated

with frequency, speed, and strength and idle time.

Frequency indicators measures how often a key is

used. Speed indicators measures how fast users type

on the keyboard. They measure the duration of a

keystroke, the duration between two keystrokes, or

the average number of keystrokes in a given time

interval. Strength measures how much pressure users

put on the keyboard (Lv et al., 2008). Idle time

measures the interval time between two input

sequences. As moods might affect users’ keystroke

behavior, these behavioral indicators might help

researchers to recognize users’ mood state (Khanna

and Sasikumar, 2010). For example, frequent usage

of such keys as “Esc”, “Alt+F4”, “Backspace” and

“Delete” might suggest that users are stressed; surge

of keystroke strength might indicate that users are

undergoing an extreme mood state.

Similar as keystrokes, mouse activity indicators

might also be helpful to pinpoint users’ mood states

(Zimmermann et al., 2003; Lee et al. 2012). Mouse

activity indicators are associated with click, scroll

and travel behaviors, which can be captured by

mouse tracking computer programs (Salmeron-

Majadas et al., 2013). Frequency indicators measure

how many clicks, scrolls and mouse moves are made

by users, and magnitude indicators measure click

speed, mouse traveling distance and web page

movements.

Another source of behavioral indicator is

clickstream data. Clickstream data are digital records

of users’ online behaviors in a chronological order

(Montgomery et al., 2004). As the data collecting

process is unobtrusive, detailed and automatic

(Bucklin and Sismeiro, 2009), clickstream data are

considered a reflection of users’ interests (Chen and

Su, 2013) and behavior (Olbrich and Holsing, 2011).

With the help of clickstream data, researchers are

able to reconstitute users’ behavioral sequences and

find out when and where behaviors are affected by

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mood states, which can potentially make mood

recognition more accurate.

2.4 Recognizing Mood

Various data classification methods such as

statistical analysis (Maehr, 2005), k-nearest

neighbours algorithms (Lv et al., 2008), discriminant

analysis (Vizer et al., 2009), support vector machine

(Vizer et al., 2009), decision tree model (Epp et al.,

2011), Bayesian network (Lee et al., 2012), and

artificial neural networks model (Khanna et al.,

2010) can be used to recognize users’ mood state.

Researchers selected methods based on the size of

samples, the type of behavioral indicator (i.e. raw

data or normalized data) and the number of moods to

by detected, so as to achieve a higher accuracy.

3 HYPOTHESIS AND RESEARCH

METHOD

3.1 Hypothesis

Our research is originated from a fundamental

question: “Can e-commerce recommender systems

be more popular if they are mood-aware?”

In the online shopping context, stress might be

one of the most important mood to consider. The

reasons are simple. Firstly, stress relief is one of the

key motivations for shopping (Arnold and Reynolds,

2003). Secondly, online shoppers are often frustrated

or confused by the excessive information displayed

on an e-commerce website. Such information

overload can create cognitive stress, which becomes

a major source of stress (Vizer et al., 2009).

Research suggests that stress can prevent us from

accepting external stimuli. Human brains can only

focus on a limited number of things at a time

(Horvitz et al., 2003). When we receive an external

stimulus, our brain quickly evaluates its relevance to

our current task, and decides whether we should

ignore or attend to it. In a stressed mood state, our

brain has less spare capacity to deal with external

stimuli. In such circumstance, recommendations are

more likely to be ignored without assessment.

E-commerce recommendations attract users’

attention by interrupting their current task with a

visual stimulus. Therefore, users’ reaction to

recommendations could be affected by stress. Based

on this finding, the authors make two hypotheses:

 H1: stress has a negative impact on users’

reaction to e-commerce recommendation

 H2: relaxed users are more receptive to the e-

commerce recommendations.

3.2 Research Method

The authors partnered with a French e-commerce

website (ECWP) to conduct the experiment, which

consists of two stages: an offline stage to develop a

mood recognition tool, and an online stage to test the

tool with real users to find out how different mood

states can affect users’ reaction to online product

recommendations (Figure 1).

Figure 1: Research Framework.

Can e-Commerce Recommender Systems be More Popular with Online Shoppers if they are Mood-aware?

175

In order to recognize the mood state of numerous

website users unobtrusively and dynamically, it is

necessary to develop an automatic mood recognition

tool. Hence, an offline experiment was conducted in

the first stage to allow the authors to develop a mood

recognition model and train it with behavioral data

with a group of users. Several conditioning technics

were used in this stage.

Regarding participant selection, 2000 registered

ECWP users were randomly selected as preliminary

candidates. Then the list was trimmed down to 100

based on their demographic features, shopping

experience with ECWP and availability. These

participants were carefully selected so that they can

represent ECWP users (Table 1).

Table 1: Composition of ECWP users and the experiment

participants.

Gender Female Male

ECWP users 53% 47%

Experiment group 50% 50%

Age

(years old)

≤25 26-35 36-50 51-60 >60

ECWP users 17% 34% 26% 19% 4%

Experiment group 20% 30% 30% 15% 5%

Total Spending

(€/year)

≤25 26-50 51-100 101-200 >200

ECWP users 71% 25% 2% 1% 1%

Experiment group 63% 29% 4% 3% 1%

Shopping Frequency

(time/year)

0 1-2 3-5 >5

ECWP users 63% 28% 7% 2%

Experiment group 52% 31% 10% 7%

When designing the offline experiment, the

authors refer to the methods used by Zimmermann et

al. (2006) and Vizer et al. (2009). To simulate

ECWP user traffic flow, experiment sessions were

scheduled at different time of the day. Participants

were split into two groups – the first one was relaxed

and the second one was stressed. In each group,

participants began by watching a video, and then

completed a series of online shopping tasks.

Relaxing and stressful video clips were used to elicit

mood, and task difficulty was adjusted based on the

type of group (i.e. relaxed or stressed) to reinforce

the desired mood. In the relaxing group, participants

were requested to assess five digital cameras (of

similar price) displayed on ECW, choose one model

to offer to their best friend and prepare a 300-word

summary in word processor to justify their choice.

They could ask questions before starting the task,

and there was no time limit to complete the task. In

the stressed group however, participants were

requested to choose a photocopier for their

organization. They must evaluate five photocopiers

of similar price provided by ECWP, choose the most

suitable model and make a formal purchase

application to their supervisor in word processor to

justify why the selected model was better than

others. Participants were told that the task must be

finished in 15 minutes, and a countdown clock was

provided as a reminder of the remaining time.

Before starting the tasks, a brief tutorial session

was conducted in both groups to introduce ECWP

website and the task to be completed. Therefore, the

lack of shopping experience with ECWP would not

be a cause of stress in the experiment. Consequently,

the stress of the stressed group comes from three

sources: 1) time stress: trial experiment indicated

that 15 minutes was barely enough to complete the

task; 2) cognitive stress: preparing formal purchase

application requires participants to be more prudent

and attentive in their work; 3) psychological stress:

the countdown clock further intensified the stress.

Participants’ behavioral data were collected by

web cookie and java-based trackers. The data were

processed by linear discriminant analysis method to

construct and validate the mood recognition tool.

Once the mood recognition tool is ready, it was

used in the second stage of the experiment to

determine when to send recommendations to users.

The second stage took two weeks, and participants

were all the ECWP users. When users started a

session on ECWP website, they were allocated

randomly, equally and unobtrusively into three

groups. Users in Group A received recommendation

as normal. The mood state of users in Group B were

assessed by the mood recognition tool in real time,

based mouse and keyboard trackers. Users received

recommendation only when they were considered

stressed. Using the same method as in Group B,

users in Group C received recommendation only

when they were considered relaxed. Their reactions

to recommended items were analyzed by statistical

approach to verify if mood can affect their reaction

to online recommendations.

The recommender system of ECWP uses a

collaborative filtering algorithm, which assumes that

users who have similar tastes will rate things

similarly. Sometimes the recommendations are

directly related to users’ current interest. Sometimes

the recommended items can be very different from

the product being viewed by users. A click on the

recommended item is defined as positive reaction to

the recommendation. Similarly, a close activity is

defined as a negative reaction.

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4 EXPERIMENT RESULT

4.1 Analysis and Result of the First

Stage

The authors use the linear discriminant analysis

(LDA) method to develop the mood recognition

function. During the experiment, a dozen of mouse

activity, keystroke, and clickstream data were

collected by trackers and cookies. These data

include session time, frequency of keyboard usage

(defined by a five second delay between two

keystrokes), frequency of keystroke, frequency of

mouse click, frequency of mouse movement,

frequency of mouse scrolling, traveling distance of

mouse, timestamp for web page open, and

timestamp for web page close. Then, these

observations were converted into comparable

variables.

Among all the 100 cases collected by the

experiment, 53 case were randomly selected as

modeling cases (23 stressed and 30 relaxed), and 47

case were used as validating cases (27 stressed and

20 relaxed). The predictors of the model were

selected based on a few considerations. Firstly, with-

in groups correlation matrix was used to minimize

collinear predictors in the model. Secondly, one-way

ANOVA test was conducted to confirm that

predictors are significantly different among groups.

Thirdly, the model’s discriminatory ability must be

acceptable, meaning that the prediction accuracy

must be acceptable not only for the modeling cases,

but also for the validating cases. Based on these

criteria, four variables were selected (Table 2).

Table 2: List of predictors.

Predictor Definition (measurement)

mvt_spd

verage distance of each mouse move

(pixel/move)

sty_pg

verage time spent stay with a page

(second/page)

clc_min

verage number of mouse click per minute

(click/minute)

dly_stk

verage time between depressing and

releasing a key (millisecond)

In the modeling cases, there were less stressed

users than relaxed users. For the authors, the main

objective of the discriminant function is to recognize

“stressed” users. Therefore, prior probabilities of

stressed and relaxed mood state were computed

according to the group sizes, so that the model can

be more conservative. The syntax to crate

discriminant model in SPSS is:

DISCRIMINANT

/GROUPS=Group(0 1)

/VARIABLES=mvt_spd time_pg clc_min

dly_stk

/SELECT=model(1)

/ANALYSIS ALL

/SAVE=CLASS SCORES PROBS

/PRIORS SIZE

/STATISTICS=MEAN STDDEV UNIVF BOXM

COEFF RAW CORR COV TABLE CROSSVALID

/CLASSIFY=NONMISSING POOLED.

Based on 53 selected cases, the discriminant model

is:

y = 0.022dl_kystr – 0.046time_pg + 0.414clic_min +

0.001mvt_speed – 6.17

Figure 2: Structure matrix.

One-way ANOVA tests indicates that the null

hypothesis can be rejected at 2% level. The within-

groups correlation matrix shows that the largest

correlation between predictors is 0.241 (correlation

between mvt_spd and clc_min). The p-value of

Box’s test of equality of covariance matrices is

0.127. Wilk’s lambda of the canonical discriminant

function is 0.509. And p-value is 0.000, suggesting

that the discriminant function is effective. The

structure matrix (Figure 2) shows that dly_stk has

the strongest discriminatory ability, followed by

sty_pg, clc_min and mvt_spd.

Classification result indicates that the 83% of the

modeling cases and 87% of the testing cases can be

correctly classified, suggesting that the model is

stable and reliable.

Figure 3: Classification results.

Can e-Commerce Recommender Systems be More Popular with Online Shoppers if they are Mood-aware?

177

4.2 Result of the Second Stage

The dataset of the two-week live experiment consists

of 52,896 sample users. The mood recognition tool

finds that 27% of them experienced stress during a

session. The statistics of the different groups are

highlighted in Table 3.

Table 3: Experiment result of the second stage.

RESULT 2

STAGE Group A Group B

Stressed

Group C

Relaxed

Sample Users 17,632 17,632 17,632

Recommendations 88,215 24,271 64,399

Stressed mood (%) - 27.17% 26.51%

Relaxed mood (%) - 72.83% 73.49%

Recommendation Click

1,411

199 1,256

Recommendation Close 2,602 796 1,848

Time to click (second)

13.55 16.12 11.05

Time to close (second) 3.48 3.17 3.79

Click through rate and close rate are used to

measure the performance of recommender system in

different mood state (Figure 4). Click through rate

measures how many displayed recommendations

end up being clicked by ECWP users. It confirms

that recommendations were more welcomed when

users were in a relaxed mood, and users in a stressed

mood showed little interest to recommendations.

Close rate measures how often ECWP users reject a

recommendation by removing it from the screen. It

indicates that stressed users were more likely to

reject recommendations actively than relaxed users.

Figure 4: Recommendation click through and close rate by

experiment group.

Behavioral data indicate that stress can affect

users’ time to react (Table 3). Compared with

relaxed users, it took longer for stressed users to

click on recommended items. And stressed users

rejected recommendations more quickly than relaxed

users. Based on these findings, we can conclude that

stress negatively affect ECWP users’ reaction to

online recommendations. In order to enhance the

usage of recommender system, recommendations

should be sent to users when they are less stressed.

5 DISCUSSION

Our findings can bring to online marketers several

key insights.

Firstly, online consumers are not always relaxed.

In some cases (e.g. 27% in ECWP), they can be

stressed. Taking care of their affective state might

help online marketers to reduce website

abandonment rate, increase deal conversion rate and

enhance consumer satisfaction. By deploying real

time mood recognition tool, online marketers can

know when to take necessary measures to relax

consumers and then help them to find what they

want. Such measure include changing the color of

website layout, propose a chat through instant

messenger and so on.

Secondly, the “right time” plays a very important

role in a successful recommendation (Fischer, 2012).

According to our findings, the right time for

recommendation is when users are in the relaxed

mood. For push-based recommender systems, mood

recognition tools can judge when to stop pushing

recommendations so as not to disturb the stressed

consumers. For pull-based online marketing

systems, mood recognition tool can help to manage

the cognitive load (i.e. the content to display) to

consumers.

Our research method has some limitations.

Firstly, the mood recognition tool was developed

based on the behavioral data of ECWP users.

Therefore, the tool might not be applicable to other

e-commerce websites. Secondly, due to the

experiment budget and time constraints, the authors

used only one machine learning method (i.e. linear

discriminant analysis) to develop the model. The

authors believe that the prediction accuracy of mood

recognition tool might be further enhanced, if other

methods (e.g. artificial neural networks) can be

tested on a larger data set. In the future research, the

authors will try to incorporate these considerations

into consideration.

In order to apply our method to the e-commerce

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practice, we suggest online merchants pay attention

to two aspects.

The first is that an e-commerce website must

obtain users’ consent before collecting their online

behavioral data unobtrusively. In our experiment,

participants were requested to sign a waiver before

being accepted. In practice, necessary modification

must be made to the website’s disclaimer, so that

consumers are given the right to accept or refuse to

be monitored.

The second is that the effectiveness of the mood

recognition tool should be reviewed from time to

time to maintain its predictive power. Users’ mood

can be affected by many different contextual factors

such as season, weather, health, motivation, or

environment. To determine the effectiveness of the

mood recognition tool, e-commerce websites can

pop up an inquiry of users’ current mood state on the

user interface, and compare users’ response with the

prediction result. If the classification accuracy is low

(e.g. less than 80%), the e-commerce website can

use the collected data to rectify the mood

recognition tool.

6 CONCLUSIONS

This paper presents a method to incorporate mood

recognition into online recommendation. Experiment

shows that quite an amount of online consumers

were in a stressed mood, and recommendations were

more popular with relaxed users than stressed users.

Such findings can be used by recommender system

developers and online marketers to improve user

experience and enhance consumer satisfaction with

e-commerce websites.

ACKNOWLEDGEMENTS

Firstly, we would like to express our gratitude to the

Science and Technology Commission of Shanghai

Municipality (STCSM) for financing this research

(STCSM Project Number: 14DZ1101400

Personalized Recommendation Technology based

Aeronautical Mobile Community Service Model

Research and Application Demonstration).

Secondly, we would appreciate the support from our

French research partner, who helped us find the

experiment participants, shared with us their

operation/business data, and provided us with the

infrastructures to conduct the experiments. Finally,

we would like to thank all the reviewers for their

valuable comments, which had made this paper

more insightful.

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