Standard collaborative filtering recommender systems assume that every account in the training data represents a single user. However, multiple users often share a single account. A typical example is a single shopping account for the whole family. Traditional recommender systems fail in this situation. If contextual information is available, context aware recommender systems are the state-of-the-art solution. Yet, often no contextual information is available. Therefore, we introduce the challenge of recommending to shared accounts in the absence of contextual information. We propose a solution to this challenge for all cases in which the reference recommender system is an item-based top-N collaborative filtering recommender system, generating recommendations based on binary, positive-only feedback. We experimentally show the advantages of our proposed solution for tackling the problems that arise from the existence of shared accounts on multiple datasets.

1. Top-­‐N
Recommenda.on
for
Shared
Accounts
RecSys’15,
Vienna
Koen
Verstrepen
Bart
Goethals
University
of
Antwerp,
Belgium
hAp://win.ua.ac.be/~adrem/bibrem/pubs/verstrepen15sharedaccounts.pdf

2. 2
CID
911
STORE
DATABASE

3. 3
CID
911
STORE
DATABASE

4. Problems:
1. Dominance
problem
2. Generality
problem
3. PresentaRon
problem
4

5. 1.
Dominance
problem
5

6. 2.
Generality
problem
6

7. 3.
PresentaRon
problem
7
?

8. No
Contextual
InformaRon
8

9. 9
Scope
definiRon
IDEALLY
REALITY
OUR
ALGORITHM
Item-­‐Based
Item-­‐Based
Item-­‐Based

10. Item-­‐based
10
• cosine
[Desphande
et
al.
2004]
• jaccard
• condiRonal
probability
• transiRon
probability
• SLIM
[Ning
and
Karypis
2011]
• BPR
[Rendle
et
al.
2009]
• dotproduct
of
latent
vectors
• …
p 2 [0, 1]
p(candidate|user) ⇠
X
purchase history
sim(purchase, candidate)
Typical recommender systems assume that every user- ac-
count represents a single user. However, multiple users often
share a single account. An example is a household in which
all people share one video-streaming account, one music-
streaming account, one online-shopping account, one loyalty
card for a store they make purchases in, etc.
Three problems arise when multiple users share one ac-
count. First, the dominance problem arises when all recom-
mendations are relevant to only some of the users that share
the account and at least one user does not get any relevant

11. Item-­‐Based
score
=
sum
of
item-­‐similariRes
11

12. Length
Adjusted
sum
of
item-­‐similariRes
12
11p
11p
11p
11p
dent(S⇤
i ) = 0.6
dent(S⇤
i ) = 0.5
p 2 [0, 1]

13. 2p
9p
11p
3p
1p
Max
over
all
subsets
13
max

14. Max
over
all
subsets
14
5p
11p
3p
3p

15. Efficiently
CompuRng
Max
15
max
All
subsets
of
count.
1. INTRODUCTION
O(2n
)
O(n log n)
Typical recommender systems assume that every user
count represents a single user. However, multiple users o
share a single account. An example is a household in w
all people share one video-streaming account, one m
Keywords: Collaborative filtering, recommender
nearest neighbors, explaining recommendations, sh
count.
1. INTRODUCTION
O(2n
)
O(n log n)
Typical recommender systems assume that every
count represents a single user. However, multiple us

16. Problems:
ü Generality
problem
2. Dominance
problem
3. PresentaRon
problem
16

17. Dominance
Problem
17

18. Problems:
ü Generality
problem
ü Dominance
problem
3. PresentaRon
problem
18

19. PresentaRon
problem
19

20. Problems:
ü Generality
problem
ü Dominance
problem
ü PresentaRon
problem
20

21. Yahoo!Music
21
Item-­‐Based,
Cosine

22. Randomly
split
22

23. Share
Account
&
Recommend
23
1. Item-­‐Based,
Cosine
2. Our
Algorithm

24. Scoring
24
OK
OK
OK
NOK
NOK
OK
NOK
NOK

25. 3
x
beAer
than
baseline
(Yahoo!Music)
25
peare in Love, The Shining, The Exorcist, Sleepy Holl
|U(a)|
(p=0.75)
Shakespeare in Love, The
|U(a)|#
users
/
account
#
NOK
/
#
users
Item-­‐Based,
Cosine
Our
Algorithm

26. IdenRfiability
of
explanaRons
26
IdenRfiability
1
¾
recommenda.on
ExplanaRon
⅗

27. 6
x
beAer
explanaRons
(Yahoo!Music)
27
ITEM-­‐BASED,
COSINE
OUR
ALGORITHM
IdenRfiability
IdenRfiability
fracRon
of
explanaRons

28. Summary
When
recommending
to
shared
accounts:
1. Generality
problem
2. Dominance
problem
3. PresentaRon
problem
SoluRon
in
case
of
item-­‐based
reference
recommenders
28

29. Artwork
from
the
Noun
Project
by
Sergi
Delgado,
Luis
Prado,
Henry
Ryder,
Nicky
Knicky,
CreaRve
Stall,
Peacock
Dream,
Andileru
Adaleru,
Jayson
Lim,
Yamini
Ahluwalia,
Andrew
Hesselden,
Okan
Benn,
John
O’Shea,
Pamela
Cocconi,
Aaron
K.
Kim,
Samuel
Fine,
Yarden
Gilboa,
Celine
Labaume,
Edward
Boatman,
Gabriele
Fumero,
Simon
Child,
Lina
Castaneda,
Le
Garage
Studio,
Travis
Beckham,
Luis
Rodrigues
29