The Effect of Subjective Speech on
Product Recommendation Virtual Agent
Tetsuya Matsui

Seiji Yamada

Seikei university
Musashino-shi, Tokyot-matsui@st.seikei.ac.jp

National Institute of Informatics, The Graduate University
for Advanced Studies (SOKENDAI)
Chiyoda-ku, Tokyo
seiji@nii.ac.jp

ABSTRACTIn this paper, we verify the effect of a VH’s objective and subjective
speech. We hypothesized that the effect of objective and subjective
speech depends on the topics that a VH speaks about and predictedthat subjective speech is effective when a VH speaks about topics
that it prefers. To verify this hypothesis, we performed an experiment with two parameters and two levels. One parameter was“persuasion strategy," and the level was “objective" or “subjective."
The other parameter was “topic," and the level was “not accordingwith preference" or “according with preference." The result showsthat the effect of subjective speech depends on the preferences of a
product recommendation virtual agent as perceived by users.

Figure 1: Experimental flow

constructed with only objective sentences, for example, “This store
opened in 1995." With this strategy, in the experiment in this paper,a PRVA made recommendations without any facial expressions andgestures. Subjective persuasion is constructed with only normative
propositions, i.e., propositions that are composed of subjective sentences, for example, “This dish at this restaurant is very delicious."
Also, with this strategy, the PRVA made recommendations withpositive facial expressions and eye movements.
Also, we introduced one other parameter, declaring preference. In
our experiment, the PRVA declared its preference to a topics before
making a recommendation, “I like to eat special local dishes during
my trip." This showed the PRVA’s preference, and we predicted thatparticipants would feel motivated by this declaration.

CCS CONCEPTS• Human-centered computing → HCI theory, concepts, andmodels;KEYWORDS
Virtual Agent, Virtual Human, Product Recommendation VirtualAgent, Persuasion Technology, Recommendation, Anthropomorphic agent
ACM Reference Format:
Tetsuya Matsui and Seiji Yamada. 2019. The Effect of Subjective Speech on
Product Recommendation Virtual Agent. In 24th International Conferenceon Intelligent User Interfaces (IUI ’19 Companion), March 17–20, 2019, Marinadel Rey, CA, USA. ACM, New York, NY, USA, 2 pages. https://doi.org/10.
1145/3308557.3308697

12

EXPERIMENTAL DESIGN

We performed the experiment with four conditions, and there were
two factors and two levels. The factors were “persuasion strategy"
and “topic." “Persuasion strategy" had two levels, “objective" and“subjective." “Topic" had two levels, “not according with preference"and “according with preference." In the experiment, participants
watched movies in which the PRVA recommended a trip to Sapporo,a Japanese city. In all conditions, the PRVA declared that “I like to eat
special local dishes during my trip." After declaring its preference, itrecommended a sightseeing spot in Sapporo with the “not accordingwith preference" level. Also, it recommended special local dishes
in Sapporo with the “according with preference" level. Figure ??
shows a snapshot of the PRVA for both the objective and subjectiveconditions.
Each movie was about 70 seconds long. We executed all movies
with MMDAgent, a toolkit for constructing virtual agent conversation systems, by the Nagoya Institute of Technology1 .
All participants answered a question, ‘How much do you wantto buy the products the PRVA recommended?", after watching themovies. The participants answered this question on a seven-point
Likert scale, and we used the answers as the scale of the recommendation effect. Figure 1 shows the experimental flow for eachtrial.

INTRODUCTION

In this paper, we focused on the product recommendation virtualagent (PRVA), a virtual agent that persuades users to purchaseproducts [4], and researched its recommendation effect, the effectof increasing the users’ buying motivation.
Persuasion technology has been widely researched in the fieldsof HCI and HAI(Human-agent interaction)[2]. VHs are widely researched as a persuasion technology[3][5][6].
In this paper, we suggest that distinctiveness is important for theeffect of PRVAs’ persuasion. We defined distinctiveness as distinguishing a persuasion strategy depending on the topic that a PRVA
speaks about. In the field of social psychology, much research has
shown that distinctiveness is important in communication[1].
In this research, we defined two persuasion strategies: “objectivepersuasion" and “subjective persuasion." Objective persuasion isPermission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for components of this work owned by others than ACM
must be honored. Abstracting with credit is permitted. To copy otherwise, or republish,
to post on servers or to redistribute to lists, requires prior specific permission and/or afee. Request permissions from permissions@acm.org.
IUI ’19 Companion, March 17–20, 2019, Marina del Rey, CA, USA© 2019 Association for Computing Machinery.
ACM ISBN 978-1-4503-6673-1/19/03. . . $15.00
https://doi.org/10.1145/3308557.3308697

1 http://www.mmdagent.jp/109

IUI ’19 Companion, March 17–20, 2019, Marina del Rey, CA, USA

Tetsuya Matsui and Seiji Yamada

4CONCLUSION

From results, we concluded that subjective speech reduced therecommendation effect when the PRVA spoke about topics thatit did not prefer. Also when the PRVA spoke about topics that it
declared a preference for, the subjective recommendations’ negativeeffect was canceled.
These conclusions suggest methods for designing PRVAs or othervirtual agents for persuasion. If it is necessary to construct virtual persuasion agents that have facial expressions and subjectivespeech, we suggest making the agents declare their preference andspeak about topics for which they declared a preference.

Figure 2: Averages of recommendation effect for each condition

ACKNOWLEDGMENTS
This research was partially supported by JSPS KAKENHI “Cognitive
Interaction Design" (No. 32626118005).

All participants were recruited from Yahoo Crowd Sourcing2 andpaid 30 yen (about 26 cents) as a reward. All trials were conductedon the Web. We obtained informed consent from all participants.
There were 120 Japanese participants in total. For the “objectivenot according with preference" condition, we recruited 55 participants; there were 40 males and 15 females ranging in age from17 to 62 years for an average of 42.6 (SD = 9.2). For the “objectiveaccording with preference" condition, we recruited 55 participants;
there were 38 males and 17 females ranging in age from 21 to 70years for an average of 43.9 (SD = 11.1). For the “subjective-not
according with preference" condition, we recruited 55 participants;
there were 33 males and 22 females ranging in age from 22 to71 years for an average of 42.9 (SD = 11.2). For the “subjectiveaccording with preference" condition, we recruited 55 participants;
there were 33 males and 22 females ranging in age from 22 to 64
years for an average of 42.6 (SD = 9.5).

3REFERENCES[1] Gerd Bohner, Elisabeth Frank, and Hans-Peter Erb. [n. d.]. Heuristic processing ofdistinctiveness information in minority and majority influence. European Journalof Social Psychology 28, 5 ([n. d.]), 855–860.
[2] Brian J Fogg. 2002. Persuasive Technology: Using Computers to Change What We
Think and Do. San Francisco: Morgan Kaufmann.
[3] Rosanna E Guadagno, Jim Blascovich, Jeremy N Bailenson, and Cade Mccall. 2007.
Virtual humans and persuasion: The effects of agency and behavioral realism.
Media Psychology 10, 1 (2007), 1–22.
[4] Lingyun Qiu and Izak Benbasat. 2009. Evaluating anthropomorphic productrecommendation agents: A social relationship perspective to designing information systems. Journal of Management Information Systems 25, 4 (2009), 145–182.
https://doi.org/10.2753/MIS0742-1222250405[5] Peter AM Ruijten, Jaap Ham, and Cees JH Midden. 2014. Investigating the influenceof social exclusion on persuasion by a virtual agent. In International Conference on
Persuasive Technology. 191–200.
[6] Hiroyuki Tokushige, Takuji Narumi, Sayaka Ono, Yoshitaka Fuwamoto, TomohiroTanikawa, and Michitaka Hirose. 2017. Trust Lengthens Decision Time on Unexpected Recommendations in Human-agent Interaction. In Proceedings of the 5thInternational Conference on Human Agent Interaction. 245–252.

RESULT AND DISCUSSIONFigure 2 shows the average of the recommendation effects for eachcondition. The results of the two-way ANOVA were persuasion
strategy: F (1, 216) = 2.812, p = 0.095, topic: F (1, 216) = 29.567, p =0.000, and persuasion strategy × topics: F (1, 216) = 3.928, p = 0.049.
There were significant differences in interaction; thus, we conducteda simple main effect test. The results were persuasion strategy
when topic was “not according with preference": F (1, 216) = 6.694,p = 0.010, persuasion strategy when topic was “according withpreference": F (1, 216) = 0.046, p = 0.830, topic when persuasion
strategy was “objective": F (1, 216) = 5.971, p = 0.015, and topic
when persuasion strategy was “subjective": F (1, 216) = 27.524,
p = 0.000.
In Figure 2, there was a significant main effect on “topic" (p <0.05), and the main effect on “‘persuasion strategy" also approachedsignificance (p < 0.1). However, the “topic" main effect was superseded by the “persuasion strategy" × “topics" interaction.
From sub-effect tests, there were significant simple main effects
for persuasion strategy when “topic" was “not according with preference," “topic" when “persuasion strategy" was “objective," and“topic" when “persuasion strategy" was “subjective." The average ofthe recommendation effect score was the maximum for the “subjective" and “according with preference" conditions. When “topic" was“not according with preference," the average of the recommendation
effect score for the “objective" level was higher than the “subjective"
level. Also, this figure shows that the recommendation effect score
for the “subjective" and “not according with preference" conditions
was significantly lower than the other conditions.

2 https://crowdsourcing.yahoo.co.jp/110