Survey of User Trust for the Video-Recommendation
Function in YouTube
Reika Miwa*, Aika Tsuchida*, Yoshinori Hijikata*
Masahiro Hamasaki** and Masataka Goto***Kwansei Gakuin University, Nishinomiya, Hyogo, Japan** National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, Japan
contact soc-research.org

Abstract. In this paper, we investigate the users’ trust against the video recommendation function of YouTube, the most popular video sharing service. The
perceived usefulness and the perceived ease of use, which are used in TAM (amodel of the user’s acceptance of new products implemented with novel technologies), are also investigated in order to clarify which factor of trust is correlated.
We conducted a crowdsourcing survey of 631 users and found that the perceived
usefulness and the perceived ease of use are highly correlated with the intention
to reuse the recommendation function and the degree of overall trust on it.
Keywords: Recommender system, Trust, TAM, YouTube.

1

Introduction

Recommendation has become indispensable for many commercial sites. However, thesuccess or failure of a recommendation service depends on whether or not users accept
the recommendation results. It is said that several factors, such as the accuracy, freshness, and popularity of the recommendation, affect the user’s acceptance of the recommendation results [1]. User’s trust in the recommender systems might also affect theuser’s acceptance of the recommendation. The accuracy and diversity of the recommendation results have been found to affect the user’s trust in the recommender system[2]. In addition, user experiments have shown that including unknown items in the recommendation results increases users’ satisfaction, although it is necessary to maintainthe accuracy of the recommendation results [3].
The interface used to present the recommendation results has also been found to affectthe user’s trust in the recommender system. Berkovsky et al. clarified the relationshipbetween the interface type and user trust through user experiments [4]. A presentationstyle of listing by genre affected the user’s trust. They also conducted a cross-culturalcomparison in four countries: the United States, Russia, France, and Japan. As a result,they identified interface components that are commonly associated with trust regardlessof culture, and those that may or may not affect trust differently depending on culture[5].

2Thus, there have been many studies on user trust in recommender systems, but mostof them have been conducted on recommender systems originally implemented for theexperiment. In other words, users evaluate the system they encounter for the first timein these studies. In commercial recommendation services, there are some services thathave already gained a large number of users and have been used for long time. Examples include Amazon.com, YouTube, and Twitter. YouTube, in particular, always
shows the recommended videos on the right side of the screen while users are viewing
videos. It can be said that users use the recommendation function on a daily basis. However, it is not clear to what extent users trust the recommender system that have alreadyacquired a sufficient number of users.
In this study, we investigate the trust that users have in the recommendation function
in YouTube, one of the large-scale commercial systems. The success of a large-scaleservice like YouTube lies in the fact that the users accept the service and decide to use
it. In the field of psychology, the Technology Acceptance Model (TAM) [6] is popular
for analyzing whether or not users will accept and use a product that employs a newtechnology. TAM presents that the user’s perceived usefulness and perceived ease ofuse for a product influence the acceptance of it. A short user experiment with a contentbased recommender system implemented for the experimentation shows that the perceived usability of the recommender system affects the user’s trust [7]. However, therelationship between each perception factor in TAM and trust in the recommender system has not been clarified. In this study, we also clarify whether these two users’ perceived factors in TAM are related to their trust in the recommender system.

2Method

We conducted a large-scale crowdsourced survey to find out the level of trust, the perceived usefulness and the perceived ease of use that users have in the recommendation
function of YouTube. The survey was conducted on April 22, 2021. A total of 501 users
after eliminating 130 unreliable users responded to the survey. They were paid 150 yenfor their responses.
As a measure of trust in the recommender system, we used Berkovsky et al.’s index,
which was developed to evaluate trust in recommender systems from multiple perspectives [4]. This index presents the user’s perceived quality of the system from the following six perspectives: competence (“recommender most knowledgeable aboutvideo”), benevolence (“recommender best reflecting my interests”), integrity (“recommender providing most unbiased suggestions”), transparency (“I understand best thereasons for the recommendations”), intention to re-use (“I would like to keep using therecommender”), and overall trust (“out of these, the most trustworthy recommender”).
Respondents responded to these items on a 7-point Likert scale. For the TAM index,
the perceived usefulness (PU) and the perceived ease of use (PE) of the users to thesystem were also asked on a 7-point Likert scale.

33

ResultWe analyzed the response data of 501 respondents. Figure 1-(a)-(f) show the histogram of trust, and Figure 1-(g) and (h) show the histogram of the perceived usefulness(PU) and the perceived ease of use (PE) for YouTube’s recommendation function. Themeans and standard deviations of the trust are shown in Table 1. In the evaluation oftrust, it was found that relatively high evaluation was obtained for benevolence (recommendation accuracy), while relatively low evaluation was obtained for integrity(recommendation fairness). The overall trust was slightly positive, and the intention to
re-use gains relatively high scores. These results show that the recommendation function in YouTube obtains relatively high trust from users.

Fig. 1. Histograms of each factor of trust and TAM
Table 1. Mean and standard deviation of each factor of trust and the Spearman’s rank correlation with the perceived usefulness (PU) and the perceived easy of use (PE).

meanSDcor PU
cor PE

Competence

Benevolence

Integrity

-0.12
1.27
0.36**
0.35**

0.71
1.19
0.52**
0.50**-0.34
1.23
0.27**
0.34**

Transparency
0.38
1.24
0.35**
0.36**

Reuse

Overall

0.49
1.41
0.66*
0.63**

0.28
1.23
0.60**
0.59****.01

The means (the standard deviations) of PU and PE are 0.73 (1.31) and 0.63 (1.32)
respectively, indicating positive evaluation. Spearman’s rank correlation coefficient between PU and PE was 0.75 (p < .01). Those including the results of non-correlations
test between each trust factor and PU (PE) are shown in Table 1 (“cor PU” and “cor4PE” rows). The results show that both PU and PE are highly correlated with both intention to reuse and overall trust. The recommendation accuracy is expected to affect thewillingness to use the system and the trust in the system. This might cause the fact thatbenevolence had the third highest correlation with PU and PE. On the other hand, competence (the knowledge of the system), integrity (fairness of the recommendation) andtransparency (explainability of the recommendation) had relatively low correlation with
PU and PE, although they had significant correlation. Since these factors are not directly related to the usefulness or the ease of use of the system, it is likely that thecorrelation was not as high as the intention to reuse and the overall trust.

4Conclusions

In this study, we investigated how much trust users have in the recommendation
function in YouTube, which is used by many users. The correlations among the perceived usefulness, the perceived ease of use, and each factor for trust were investigated.
The results of the study showed that the recommendation function of YouTube is relatively highly trusted. In addition, the perceived usefulness and the perceived ease of use
in TAM were found to be highly correlated with intention to reuse and overall trust in
the recommendation function. One of the challenges of this study is that user behavior
on YouTube have not been measured, and we have not obtained data to predict whichusers are likely to have trust in the recommendation function. In the future, we will
investigate users’ video selection behavior in response to YouTube’s recommendation
results to clarify how trust affects the response to recommendations.

Acknowledgement
This work was supported by JST, CREST Grant Number JPMJCR20D4, Japan.

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