Explainable Recommendation for Repeat Consumption
Kosetsu Tsukuda

Masataka Goto

National Institute of Advanced Industrial Science andTechnology (AIST), Japank.tsukuda@aist.go.jp

National Institute of Advanced Industrial Science andTechnology (AIST), Japanm.goto@aist.go.jp

ABSTRACTDisplaying appropriate explanations for recommended items is ofvital importance for improving the persuasiveness and user satisfaction of recommender systems. Although a user often consumes thesame item repeatedly in some domains such as music and restaurants, existing studies have focused on generating explanations forrecommending novel items. In this paper, we describe the conceptof explainable recommendation for repeatedly consumed items.
Because of the high proportion of repeat consumption in music
listening, we suggest nine kinds of explanations for song recommendations according to three factors: personal, social, and item factors.
From the results of an online survey involving 622 participants, weevaluate the usefulness of these explanations.

CCS CONCEPTS• Information systems → Recommender systems.

KEYWORDS
Explainable recommendation; Repeat consumption; User study
ACM Reference Format:
Kosetsu Tsukuda and Masataka Goto. 2020. Explainable Recommendation
for Repeat Consumption. In Fourteenth ACM Conference on Recommender
Systems (RecSys ’20), September 22–26, 2020, Virtual Event, Brazil. ACM, New
York, NY, USA, 6 pages. https://doi.org/10.1145/3383313.3412230

1

INTRODUCTION

To improve the persuasiveness and user satisfaction of recommender systems (RSs), it has become an essential research topic
to provide explanations for recommended items so that users canunderstand why they are recommended [25]: this approach is called
explainable recommendation (ER) [29]. In ER, the explanations displayed to a user are typically generated from her histories of item
consumption [28]. Various user studies have reported that ER enables users to more accurately select items [5] and improves user
acceptance of recommended items [10].
Despite the various kinds of approaches for generating explanations in previous studies [28], most of them focused on generating
explanations for recommending novel items to a user. However, in
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https://doi.org/10.1145/3383313.3412230

some domains such as music listening, video watching, and restaurant visiting, a user often consumes the same item repeatedly overtime [2, 4], and such user behavior is called repeat consumption.
Therefore, it is worth considering explanations for recommending
items that a user has already consumed. For example, suppose thatexactly five years have passed since a user listened to Shake It Off
by Taylor Swift for the first time on a music streaming service. Inthat case, the service could recommend the song to the user withan explanation of “We recommend Shake It Off because exactly
five years have passed since you listened to it for the first time.”
In typical repeat consumption, a user initially consumes an item
repeatedly within a short time span; but the span gradually increases as she becomes bored with the item, and eventually she
stops consuming it [4]. Even if the user has stopped listening to
Shake It Off as a result of repeat consumption, however, the musicstreaming service could increase the possibility that she listens to it
again by displaying an explanation like the one above. For example,
listening to the song could enable her to feel nostalgia by thinking
back to those days when she often listened to it. This would also
be beneficial for the music streaming platform, because it could
increase its revenue by encouraging users to listen to not only novel
songs but also already consumed songs. Despite such possibilities,
no studies have focused on ER for repeat consumption.
In light of the above, in this paper, we provide the first study of
ER for repeat consumption. To generate explanations for consumed
items, we consider the following three factors.
• Personal factor: this factor considers the interaction between atarget user and a recommended item. For example, in the context
of restaurant visiting, one possible explanation is “Exactly sevenyears have passed since you visited the recommended restaurant
for the first time.”• Social factor: this factor considers the interaction between allusers on a service and a recommended item. For example, inthe context of video watching, one possible explanation is “Justnow, the number of unique users watching the recommended
video reached exactly 100 thousand.”• Item factor: this is an item-specific factor that is not related tothe users who consumed the item. For example, in the context
of music listening, one possible explanation is “Today, the artist
performed the recommended song at a live concert.”
According to these factors, we suggest nine kinds of explanations
and evaluate their effectiveness through the results of an online
survey.
Our contributions in this paper can be summarized as follows.
• To the best of our knowledge, this is the first study to propose
the concept of ER for repeat consumption. We suggest nine
kinds of explanations based on the above three factors: personal,social, and item factors.

RecSys ’20, September 22–26, 2020, Virtual Event, Brazil• To investigate the effectiveness of our proposed explanations,
we conducted an online survey involving 622 participants inthe domain of song recommendation. In the survey, first, weinvestigated the persuasiveness of the nine kinds of explanations.
Second, because some explanations include a variable such as atime span or play count, we also evaluated the value preference
for such variables.
• From the survey results, we reveal various insights for both thepersuasiveness (e.g., explanations related to song popularity are
more persuasive) and the value preference (e.g., a clear tendency
of preferable values exists for all kinds of explanations). We
also discuss how to apply these insights to repeat consumption
recommendations.

2 RELATED WORK
2.1 Explainable Recommendation
Studies have reported that displaying explanations in RSs can increase persuasiveness [10] and user satisfaction [26]. One approach
for ER is single-style explanations in which an RS displays explanations that involve a single source of data such as user-based or
item-based similarity. Studies by Herlocker et al. [10], Chang etal. [7], and Symeonidis et al. [24] are classified into this approach.
Another approach is hybrid explanations that combine multiple
styles. That approach has been reported to be more effective than
the single-style approach [19]. Although most studies on hybrid
explanations have focused on visualizing various explanation styles
via graphical user interfaces (GUIs) [6, 11, 12, 18, 20, 27], Kouki etal. [16] recently showed that text-based explanations are more persuasive than GUIs-based ones. They generated explanations basedon a hybrid RS, called HyPER [14]. Unlike prior studies that aimed
to generate explanations of novel items for a user, we focus hereon explanations for repeat consumption. Inspired by the aforementioned insights of prior studies, in this paper, we adopt text-based
explanations that can be generated in the HyPER framework as
described in Section 3.1.

2.2

Repeat Consumption

Although most RS studies have traditionally aimed to recommend
novel items to a user [1], users are known to consume the same item
repeatedly over time in various domains. For example, in music
listening and location check-ins, averages of 69% and 51% of a user’s
consumption, respectively, consist of items already consumed by
the user [4]. Because of such popularity of repeat consumption,
several studies have developed models for simulating repeat consumption behavior [2, 4, 8] or proposed methods for recommending
previously consumed items [9, 13, 21, 22]. By analyzing repeat consumption behavior, Benson et al. [4] reported that each item has
its own lifetime for a user: at the beginning of the lifetime, thetemporal gap between item consumption events is small; at the endof the lifetime, however, the gap becomes large, and eventually theuser becomes bored with the item. Such boring items are usually
removed from the recommendation candidates. However, we believe that if we recommend these boring items with explanations,they might be consumed again by the user, and this would also bebeneficial for the service platform to increase its revenue. Hence,
our study is a first step toward investigating whether some types

Kosetsu Tsukuda and Masataka Gotoof explanations are persuasive enough to motivate a user to revisit
already consumed items.

3

EXPLANATIONS FOR REPEATCONSUMPTION RECOMMENDATIONIn this section, we first give an overview of the HyPER framework [14] and describe how to apply it to our problem. We thendescribe the three factors that we consider and nine kinds of explanations for repeat consumption recommendation.

3.1

Overview of HyPER

HyPER [14] is a hybrid RS that uses probabilistic soft logic (PSL) [3]and develops recommendation models through a set of rules. An
example of a rule is “If user u has listened to song s 1 and s 1 is similar
to song s 2 , then u would listen to s 2 .” HyPER automatically learns tobalance the different rules and computes the probability that a user
accepts each item. It then generates explanations from explanationstyles, each of which corresponds to a rule. For example, the aforementioned example rule is used for an item-based explanation style,which generates a concrete explanation like “We recommend Shake
It Off because it is similar to King of Anything, which you like.” Onecharacteristic of HyPER is its extensibility: it can incorporate any
kind of style that can be written as a rule. By taking advantageof this, in the following subsection, we propose nine explanationstyles and their rules to support recommendation explanations for
repeat consumption.

3.2

Explanation Styles

In this paper, we assume a scenario in which an item that a user
has already consumed is recommended to her. Under this scenario,
we aim to display an explanation of why the item is recommended
for repeat consumption. To this end, we consider three factors involving the item: personal, social, and item factor. We use song
recommendation as a target domain, because repeat consumption
is especially salient in music listening [4]. Although we describe
a total of nine explanation styles with example explanations, as
listed in Table 1, we acknowledge that there can be other explanation styles. However, note that our goal here is not to list all
possible explanation styles; rather, we aim to suggest examples ofpossible explanations based on the three factors. We believe thatthese factors can help any service platform to consider explanations
for recommending consumed items according to service-specificcharacteristics.
3.2.1 Personal Factor. The personal factor considers the interaction between a target user and a recommended song. As the user
repeatedly listens to the same song, the service platform storeslogs. By using the logs, we propose four explanation styles for the
personal factor: P-first, P-last, P-together, and P-total. P-first andP-last are related to the elapsed time: P-first uses a rule of “If exactly x years have passed since user u listened to song s for the firsttime on the service, u would listen to s”; P-last is based on a ruleof “If exactly x years have passed since user u listened to song s
last, u would listen to s.” Here u is the target user, and s is a song
repeatedly consumed by u. In the example explanations of Table 1,

Explainable Recommendation for Repeat Consumption

RecSys ’20, September 22–26, 2020, Virtual Event, Brazil

Table 1: Nine explanation styles and example explanations.
Factor
PersonalfactorSocialfactorItemfactorStyle
P-firstP-lastP-together
P-totalS-totalS-uniqueS-favorite
I-releaseI-liveWe recommend Shake It Off by Taylor Swift because:
Exactly five years have passed since you listened to it for the first time.
Exactly three years have passed since you listened to it last.
Around the same time, you frequently listened to it and Applause by Lady Gaga, which you listened to just now.
You will have played it 100 times when you listen to it next.
Its total play count by all users reached one million.
The number of unique users who listened to it reached 100 thousand.
The number of users who added it to their Favorites reached 10 thousand.
Exactly five years have passed since it was released.
Today, Taylor Swift performed it at a live concert.

words in italics are variables. Therefore, five years in the explanation of P-first could be three years, seven years, or even 10 years. One
possible way to set appropriate values would be to enable users to
set them according to their personal preferences. It would also be
possible for the music streaming service to set particular values incommon for all users. We evaluate the preferences for these values
in Section 4.3. As for P-together, the explanation is generated fromthe co-occurrence of songs in a user’s play history. Let s ′ denote
another song that has been repeatedly consumed by u. Then, therule is “If u listened to s ′ just now and has frequently listened to s ′along with s, u would listen to s.” The co-occurrence of songs can bedetected by using session information [23] in the user’s play logs.
We assume that these three styles would be persuasive because theywould enable the user to feel nostalgia by thinking back to thosedays when she often listened to the recommended song. Finally,
P-total considers personal play counts, with a rule of “If u’s total
play count for s would reach x by playing s next, u would listen tos.” We think that this style would also be persuasive, because theuser would feel a sense of achievement if she knew the play countinformation.
3.2.2 Social Factor. The social factor considers the interaction
between all users on the service and a recommended song. We
suggest three styles based on this factor: S-total, S-unique, andS-favorite. S-total uses a rule of “If s’s total play count by all users
has reached x, u would listen to s,” while S-unique adopts a rule of“If the number of unique users who have listened to s has reached x,
u would listen to s.” As for S-favorite, on music streaming services,
users can usually use a Favorites function that enables them to markany song as a favorite. Thus, S-favorite is based on a rule of “If thenumber of users who added s to Favorites has reached x, u would
listen to s.” Our assumption on the persuasiveness of these styles isthat the user would want to listen to s again if she knew a familiarsong had been played or chosen as a favorite by so many users.
3.2.3 Item Factor. Lastly, the item factor considers item-specificinformation that is independent of users on the music streaming
service. The first style, I-release, adopts a rule of “If exactly x years
have passed since s was released, u would listen to s.” We assume
that this style would be persuasive because such information would
make the user think of the song’s anniversary. For the second style,I-live, we assume that the user would want to listen to a song thatthe artist recently performed at a concert because she would enjoy

a “live” feeling by listening to it. Therefore, this style uses a rule of“If the artist performed s at a live concert x days ago, u would listen
to s.” One way to collect concert-related information is by mining
Twitter, because it is common for concert attendees to tweet about
performed songs [17].

4USER STUDY

In this section, we answer the following two research questions byconducting an online survey involving 622 participants.
RQ1 How do the explanation styles affect the explanation persuasiveness?
RQ2 For each explanation style, is there any tendency in terms
of preferred values of the variable in the explanation?

4.1

Participants

We recruited 679 participants for our survey via an online researchcompany. All the participants were Japanese and listened to music at
least one day per week via any online music streaming service. The
participants answered our questionnaire through a web browser.
We paid about 13.9 USD (1,500 JPY) to each participant. Although
679 participants joined the survey, to make the analysis results
more reliable, we removed the answers from 57 participants: 39 ofthem gave the same answer to all questions (e.g., choosing “1” forall questions), and 18 of them submitted improper responses to an
open-ended answer format (e.g., “xxxxxx”). Of the remaining 622
participants, 296 were male (10s: 8; 20s: 60; 30s: 82; 40s: 73; 50s: 73),
and 326 were female (10s: 10; 20s: 67; 30s: 91; 40s: 82; 50s: 76).

4.2

Persuasiveness of Explanation Styles

First, to answer RQ1, we investigated the persuasiveness of each of
the nine explanation styles introduced in Section 3.2. In this investigation, we aimed to evaluate the persuasiveness purely according
to the explanation style. To this end, each explanation described therecommended song as “song A” to remove any bias regarding thesong. In addition, to remove any bias regarding particular valuesof the time (P-first, P-last, I-release, and I-live), play count (P-total
and S-total), or user count (S-unique and S-favorite), we asked theparticipants to assume any values they liked. Thus, in the case of
P-first, for example, we showed the participants a description likethe following.

RecSys ’20, September 22–26, 2020, Virtual Event, Brazil

Kosetsu Tsukuda and Masataka Goto

On a music streaming service, you have previously listened
to song A. One day, song A is recommended to you with thefollowing explanation: “Song A is recommended to you because
exactly five years have passed since you listened to it for the
first time on this service.” Here, “five years” is just an example,
and you can assume any time span you like, such as half a year,
one year, or seven years.1
For each explanation style, the participants were asked to rate its
persuasiveness on a scale of 1 to 7 (1: not persuasive at all; 7: very
persuasive). The explanation styles were displayed in a random
order to each participant. For participants who gave an explanationstyle a rating of 5-7 (i.e., those who thought that the explanationstyle was persuasive), we also provided an open-ended answer
format for freely describing why they thought it was persuasive.
Figure 1 shows the mean persuasiveness for each explanationstyle. It can be observed that the nine styles can be divided into
two groups: a high-persuasiveness group (hereafter, “High group”)
that includes P-together, P-total, S-total, S-unique, S-favorite, andI-live, and a low-persuasiveness group (hereafter, “Low group”) that
includes the remaining three styles. Because the persuasiveness ofany style in the High group is statistically higher than that of any
style in the Low group at p < 0.05 by Tukey’s HSD test, a clear
difference in persuasiveness exists between the two groups.
In the High group, P-together has the highest persuasiveness:the value is statistically higher (p < 0.05) than those of the remaining seven styles except for S-total. As expected, the most popular
reason for persuasiveness was about nostalgia. Although the mostpopular reasons for P-first and P-last were also about nostalgia,
as we had assumed, both styles belong to the Low group. In thesituation of P-together, the user can continuously listen to nostalgic
songs (i.e., one played by her and another recommended by theservice), while in P-first and P-last, the user feels nostalgia only
from the recommended song. Thus, P-together would enable theuser to more deeply feel nostalgia, resulting in its higher persuasiveness. For P-total, the most popular reason was that the user
could realize again, when she knew her total play count, how much
she loved the song. Our expected reason (that she could feel a sense
1 If a user does not remember that she has listened to song A, it should be recommended

as a novel song. Therefore, in this survey, we asked the participants to assume thatthey remembered the recommended song. One approach to distinguish whether auser remembers a song would be to refer to its play count; we leave evaluation of thatapproach as future work.

0.4
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0.76
0.68 0.69
0.36 0.34 0.35
0.39 0.33 0.39 0.38
S-u

Figure 1: Mean persuasiveness for each explanation style,with standard errors.

0.6

0.39
0.37
0.36
0.38
0.35

S-to

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I-live

0.44
0.31
0.29
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PersuasivenessP-together

P-la

P-last4.4

Figure 2: Pearson’s correlation of persuasiveness between
explanation styles. All correlations are significant at p <0.001.

of achievement) was the second most popular. Regarding the social factor, all explanation styles (S-total, S-unique, and S-favorite)
are in the High group, and no significant difference was observed
between their persuasiveness. For all of these styles, the reason
with the highest frequency was that the user could perceive thepopularity of the song. More specifically, in the case of S-total, forexample, a participant answered, “I become happy to know that afamiliar song has been played so many times, so I want to listen
to the recommended song.” Finally, for I-live, in addition to ourassumed reason (that the user could enjoy a “live” feeling), anotherreason related to the artist’s desire (e.g., “A song performed at a live
concert is one that the artist wants people to hear, so I want to listen
to it.”) was also popular. Given a target user, the explanation styles
that are persuasive enough to her should be used to recommend
songs. To save the cost of such personalization, however, it would
also be beneficial for music streaming platforms to simply use styles
in the High group.
Figure 2 shows the Pearson’s correlation of persuasiveness between explanation styles. P-first, P-last, and I-release have relatively
high correlation values with each other. This means that, although
they belong to the Low group, there was a group of participants who
regarded the time-related styles as persuasive. Therefore, a service
platform can estimate that if a user listens to a song recommended
according to a time-related style, she would also accept songs recommended by other time-related styles. We can also observe anothergroup of participants who especially prioritized popularity-related
styles, because S-total, S-unique, and S-favorite have high correlation values with each other. On the other hand, the correlation
values between the time and popularity-related styles are relatively
low. That is, the two participants groups were exclusive to someextent.

4.3

Value Preference in Explanations

Except for P-together, explanations generated from the other eightstyles have variables related to time (P-first, P-last, I-release, andI-live), the play count (P-total and S-total), or the user count (Sunique and S-favorite). To answer RQ2, we investigated the value
preferences for the variables in these styles. For each explanation

Explainable Recommendation for Repeat Consumption

RecSys ’20, September 22–26, 2020, Virtual Event, Brazil

P-first (190)6

P-last (185)P-total (246)

S-total (272)

5
4

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S-unique (249)6

S-favorite (242)

I-release (169)

I-live (257)

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3

Figure 3: Mean preferences for each candidate value of a variable. The range of the y-axis is the same for all charts. The
numbers in parentheses indicate the number of participants who gave their preference.
style, four or five candidate values of the variable were displayed
to the participants. For example, for P-total, the candidate values
were 50, 100, 300, 500, and 1K times. For each value, the participants
rated their preference on a scale of 1 to 7 (1: not preferable at all; 7:very preferable). We also provided an open-ended answer format
for describing the reason for a preference. For each style, we asked
for preference rating from participants who gave a rating of 5-7 forthe style’s persuasiveness, as described in Section 4.2.
For all eight styles with variables, Figure 3 shows all candidate
values and the mean preference for each value. For all styles, themean value monotonically increases or decreases as the candidate
value increases (x-axis). These results indicate a clear tendency
toward a value preference. According to the free descriptions of
the reasons for these preferences, in the cases of P-first and Plast, for example, the participants preferred a longer time spanbecause it provided a stronger feeling of nostalgia. Similarly, for the
popularity-related styles (S-total, S-unique, and S-favorite), higher
values were preferred because they enabled a stronger feeling of asong’s popularity. In contrast, for I-live, the participants preferreda shorter time span, because by listening to the recommended song
within a shorter time span, they could feel stronger identification
with the artist. From these results, for each explanation style, amusic streaming platform could set a variable value whose mean
preference is higher than, say, 4.0.

5

CONCLUSION

In this paper, we proposed the concept of explainable recommendation for repeat consumption. We suggested nine explanation styles
and conducted an online survey to investigate the persuasivenessof these styles and value preferences in explanations. Here, we
acknowledge some limitations of this study: (1) we investigatedonly the domain of song recommendation; (2) we manually generated non-personalized explanations; and (3) in the online survey,
all participants were Japanese. Nonetheless, we believe that thisstudy is a worthwhile contribution to the ER field as a first step
toward investigating the usefulness of ER for repeat consumption.
Moreover, these limitations indicate the possibilities of this research
topic and can guide future work. In fact, initial studies on ER for
hybrid RSs also had limitations (1) [16] and (2) [15], but those studies contributed to later studies. Similarly, limitation (3) can lead tofuture work such as investigating the differences in persuasivenessfor each explanation style among countries.

ACKNOWLEDGMENTS
This work was supported in part by JSPS KAKENHI Grant Number
20K19934 and JST ACCEL Grant Number JPMJAC1602, Japan.

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