In D. Reitter & F. E. Ritter (Eds.), Proceedings of the 14th International Conference on Cognitive Modeling(ICCM 2016). University Park, PA: Penn State.

Eye-tracking Analysis for Product Recommendation Virtual Agent with MarkovChain Model
Tetsuya Matsui (tmatsui@nii.ac.jp)

National Institute of Informatics
2-1-2 Hitotsubashi, Chiyoda Tokyo 101-8430, Japan

Seiji Yamada (seiji@nii.ac.jp)

National Institute of Informatics/Sokendai/Tokyo Institute of Technology
2-1-2 Hitotsubashi, Chiyoda, Tokyo 101-8430, Japan
Keywords: eye-tracking, product recommendation virtualagents, Markov chain, Shanon entropy, transition matrix, stationary distribution, fixation duration

Introduction
PRVAs, product recommendation virtual agents, are agentsthat are designed for virtual clerks in online shopping.
Prendinger et al. investigated the effect of virtual clerks byeye tracking analysis (Prendinger, Ma, & Ishizuka, 2007).
In their experiment, participants were introduced real estate
properties by text, speech, and an animated agent. They
showed that the agent’s use of deictic gestures had the effectof attracting a participant’s gaze. Terada et al. studied what
appearance was the most suitable for PRVAs (Terada, Jing, &Yamada, 2015). They showed that one of the most effective
appearances were dog, robot, and young woman. In this paper, we investigated the effect of PRVA’s emotion transition
to user’s gaze by eye tracking analysis.
A Markov chain model is widely used for constructing amodel of eye tracking transition. Liechty et al. showed local
and global covert visual attention by adapting a Bayesian hidden Markov model (Liechty, Pieters, & Wedel, 2003). He etal. suggested investigating hidden user behaviors that occur
when a user is using a search site by using a partially observable Markov model with duration (POMD) (He & Wang,2011). This model is derived from the hidden Markov model(HMM). The difference was that POMD contained a partiallyobservable event. He et al. suggested that only seeing without
clicking links was the hidden user behavior.
In this paper, our goal was to improve the PRVA design methodology by analyzing user eye-tracking data. We
focused on transition-based analysis. In prior research on
human-agent interaction, eye-tracking data were mainly analyzed on the basis of fixation durations. This is the mostimportant method in this paper.

Markov chain
In our research, we used the Markov chain model for analyzing the fixation transitions between areas of interest (AOIsequence). The Markov chain satisfies the following equation, where Xn is a random variable and n means time step
(Brooks, Gelman, Jones, & Meng, 2011).
239

P(Xn+1 = xn+1 | Xn = xn , · · · , X0 = x0 )= P(Xn+1 = xn+1 | Xn = xn )(1)

In this research, our goal was to compare the transition entropy and the stationary entropy of the AOI sequence

ExperimentParticipants
Fifteen Japanese participants joined in the experiment. Therewas eight males and seven females, and they were aged between 20 and 39, for an average of 29.3 (SD = 6.9). Due to
not getting sufficient gaze data, we omitted the data of one
male participant.

Task
The PRVA recommended 10 package tours to Japanese castles. These castles were built in the Japanese Middle Ages,from about the 13th to 16th century. The PRVA made recommendations successively, and the recommendation order wasrandom. For the first half of the recommendations, the PRVA
kept a poker face without making any gestures. We defined
this agent as the apathy agent. In the latter half, the PRVA
smiled and made cute gestures. We defined this agent as thepositive agent. This change in facial expressions and gestures
expressed the agent’s emotion transition, and we aimed forthe agent’s positive emotion to infect participants.
The PRVAs were executed with MMDAgent1 . This is afree toolkit for constructing agent systems with speech. It
contains the agent character “Mei” and is distributed by theNagoya Institute of Technology. We also used the text tospeech software VOCELOID+ Yuduki Yulari EX22 for theagent’s voice.

Apparatus
We carried out experiments with Tobii Pro X2-60 and a 30inch LCD monitor (1920 ʷ 1200 resolution). Eye movements were recorded at a 60-Hz sampling rate. All participants were requested to sit down in a chair at a 60-cm distance from the monitor during the experiment. All stimuli
1 http://www.mmdagent.jp/

2 http://www.ah-soft.com/voiceroid/yukari/

Discussion
From πa and πp , we can find few definite differences. Themost different element was p3 between these two matrices.
In πa this means 0.05, and in πp , this means0.098. This coordinate means the percentage of probability that fixation transitions to “face” when the fixation is on “face” one time-step
before. This shows that implementing the positive emotion
caused participants’ fixations to stay on the agent’s face. Thisphenomenon proves that the participants felt more humanlikeness with the agent (Strait, Vujovic, Floerke, Scheutz, &Urry, 2015).
Figure 1: Defined AOIs

were presented on the monitor, and all participants listened tothe recommendations with headphones. To construct a transition matrix and stationary distribution, we used the R package“markovchain” (Spedicato, Kang, & Yalamanchi, n.d.).

Analysis method
We defined the AOIs as shown in Figure 1. We divided thepresented stimuli area into five areas (“background,” “body,”“face,” “image,” and “text”). We analyzed based on the fixation order. Fixation order meant the path of a participant’s fixations, and we counted the number of transitions of the AOIs
that the participants fixed on (including self transitions). We
constructed the transition matrix and stationary distribution
from this analysis. The minimum fixation duration was 60
ms, and transition advanced one step when fixation occurred.

Results
We constructed the transition matrix and stationary distribution from the fixation order of the first half of the recommendations. We calculated each transition matrix from each recommendation. We got 10 transition matrices from one participant and got 140 transition matrices in total. We calculated
the average of all matrix elements. This was for the “transition matrix derived apathy agent”.
Also, we calculated the stationary distribution from thismatrix. This was for the “stationary distribution derived apathy agent” (πa = (0.22, 0.11, 0.05, 0.26, 0.37)). On the matrix, each coordinate means these AOIs: 1 = “background,”
2 = “body,” 3 = “face,” 4 = “image,” and 5 = “text.” In thestationary distribution, the same coordinate means the same
AOI .
We constructed the transition matrix and stationary distribution from the latter half of the recommendations in thesame way. These were for the “transition matrix derived
positive agent” and “stationary distribution derived agent”(πp = (0.22, 0.16, 0.098, 0.21, 0.31)). The same coordinate
means the same AOI in πa .
240Conclusion
There is demand for PRVAs that have the ability to attract
a user’s attention to products or to themselves. This can berephrased as the ability to attract and keep a user’s fixation on
the images of products or agents. We investigated the effectof implementing a positive emotion in a PRVA by analyzing eye-tracking and aimed to adapt the result to the modelof designing PRVAs that attract a user’s fixation. From ourexperiment, a positive emotion attracted participants’ gaze tothe agent’s face. This suggest a methodology of attracting orkeeping a user’s gaze and buying motivations.

Acknowledgment
This study was partially supported by JSPS KAKENHI
ʠ Cognitive Interaction Design ʡ(No.26118005).

References
Brooks, S., Gelman, A., Jones, G., & Meng, X.-L. (2011).
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