Presented at the 14th ACM Conference on Recommender Systems (RecSys 2020) in September 2020.

1. Explainable Recommendation for Repeat Consumption
Difference between existing studies and ours
3 factors to generate explanations for consumed items
Online survey involving 622 participants
National Institute of Advanced Industrial Science and Technology (AIST)
Kosetsu Tsukuda and Masataka Goto
Factor Style We recommend “Shake It Off” by “Taylor Swift” because:
Personal
factor
P-first Exactly five years have passed since you listened to it for the first time.
P-last Exactly three years have passed since you listened to it last.
P-together Around the same time, you frequently listened to it and “Applause” by “Lady Gaga”, which you listened to just now.
P-total You will have played it 100 times when you listen to it next.
Social
factor
S-total Its total play count by all users reached one million.
S-unique The number of unique users who listened to it reached 100 thousand.
S-favorite The number of users who added it to their Favorites reached 10 thousand.
Item
factor
I-release Exactly five years have passed since it was released.
I-live Today, “Taylor Swift” performed it at a live concert.
How do the explanation styles affect the
explanation persuasiveness?
 Persuasiveness of any style in the High group is
statistically higher than that of any style in the Low group
 It would be beneficial for a music streaming platform to
simply use styles in the High group
Explanation
Consumed items Novel items
Explanation
Consumed items Novel items
Existing studies:
generate explanations for recommending
items that are novel to a user
Our study:
generate explanations for recommending
items that a user has already consumed
Item factor
Personal factor considers
the interaction between a target
user and a recommended item
Social factor considers
the interaction between all users
and a recommended item
Item factor is an Item-specific factor
that is not related to the users who
consumed a recommended item
Personal factor Social factor
Is there any tendency in terms of preferred
values of the variable in the explanation?
High group
Low group
 For all styles, the mean value monotonically increases
or decreases as the candidate value increases (x-axis)
 A music streaming platform could set a variable value
whose mean preference is higher than, say, 4.0
RQ1 RQ2
14th ACM Conference on Recommender Systems (RecSys 2020)
Words in italics are variables