Recommendation Independence The 1st Conference on Fairness, Accountability, and Transparency Article @ Official Site: http://proceedings.mlr.press/v81/kamishima18a.html Conference site: https://fatconference.org/2018/ Abstract: This paper studies a recommendation algorithm whose outcomes are not influenced by specified information. It is useful in contexts potentially unfair decision should be avoided, such as job-applicant recommendations that are not influenced by socially sensitive information. An algorithm that could exclude the influence of sensitive information would thus be useful for job-matching with fairness. We call the condition between a recommendation outcome and a sensitive feature Recommendation Independence, which is formally defined as statistical independence between the outcome and the feature. Our previous independence-enhanced algorithms simply matched the means of predictions between sub-datasets consisting of the same sensitive value. However, this approach could not remove the sensitive information represented by the second or higher moments of distributions. In this paper, we develop new methods that can deal with the second moment, i.e., variance, of recommendation outcomes without increasing the computational complexity. These methods can more strictly remove the sensitive information, and experimental results demonstrate that our new algorithms can more effectively eliminate the factors that undermine fairness. Additionally, we explore potential applications for independence-enhanced recommendation, and discuss its relation to other concepts, such as recommendation diversity.

1. Recommendation Independence
Toshihiro Kamishima*, Shotaro Akaho*, Hideki Asoh*, and Jun Sakuma**
www.kamishima.net
*National Institute of Advanced Industrial Science and Technology (AIST), Japan
**University of Tsukuba; and RIKEN Center for Advanced Intelligence Project, Japan
1st Conference on Fairness, Accountability, and Transparency (FAT* 2018)
@ New York, USA, Feb. 24, 2018
1
START

2. Outline
2
Basics of Recommendation
recommendation tasks, collaborative filtering
Recommendation Independence
sensitive feature, recommendation independence
Applications of Recommendation Independence
adherence to laws and regulations, fair treatment of content
providers, exclusion of Unwanted Information
Independence-Enhanced Recommendation
regularization approach, model-based approach
Conclusions & Future work
We overview our series of researches on fairness in recommendation
rather then focusing on this paper

3. 3
Basics of Recommendation

4. Recommender System
4
Recommenders: Tools to help identify worthwhile stuff
[Konstan+ 03]
Find Good Items Predicting Ratings
[Herlocker+ 04, Gunawardana+ 09]
✽ Screen-shots are acquired from Amazon.co.jp and Movielens.org on 2007-07-26
Ranking items according to
users' preference, to help for
finding at least one target item
Presenting items with
predicted ratings for a user, to
help for exploring items

5. Collaborative Filtering
5
✽ There are other approaches: content-based filtering or knowledge-based filtering
Collaborative filtering is a major approach for predicting users'
preference in a word-of-mouth manner
recommending items liked by those who having similar preferences
Any good
sushi
restaurant?
I'll go to
the “Taro”
The “Taro”
is awesome
They like
the “Taro” restaurant
people who like
similar tastes of sushi
I like the “Taro”

6. 6
Recommendation Independence

7. Sensitive Feature
7
S : sensitive feature
This represents information that should be ignored in a
recommendation process
Its values are determined depending on a user and/or an item
As in a case of standard recommendation, we use random variables
X: a user, Y: an item, and R: a recommendation outcome
A sensitive feature is restricted to a binary type
We adopt a variable required for recommendation independence
Ex. Sensitive feature = movie’s popularity / user’s gender

8. Recommendation Independence
8
No information about a sensitive feature influences the outcome
The status of the sensitive feature is explicitly excluded from the
inference of the recommendation outcome
Recommendation Independence
statistical independence
between a recommendation outcome, R, and a sensitive feature, S
Independence-Enhanced Recommendation
Preferred items are predicted
so as to satisfy a constraint of recommendation independence
Pr[R | S] = Pr[R] ≡ R ⫫ S
[Kamishima+ 12, Kamishima+ 13, Kamishima+ 16, Kamishima+18]

9. dislike likedislike like
Effect of Independence Enhancement
9
Standard Independence-enhanced
two distributions are
largely diverged
two distributions
become closer
The bias that older movies were rated higher
could be successfully canceled by enhancing independence
✽ each bin of histograms of predicted scores for older and newer movies
a sensitive feature = whether a movie is newer or older

10. 10
Applications of
Recommendation Independence

11. Adherence to Laws and Regulations
11
[Sweeney 13]
A recommendation service must be managed
while adhering to laws and regulations
suspicious placement in keyword-matching advertisements
Advertisements indicating arrest records were more frequently
displayed for names that are more popular among individuals of
African descent than those of European descent
↓
Socially discriminative treatments must be avoided
sensitive feature = users’ demographic information
↓
Legally or socially sensitive information
can be excluded from the inference process of recommendation

12. Fair Treatment of Content Providers
12
System managers should fairly treat their content providers
The US FTC has investigated Google to determine whether the search
engine ranks its own services higher than those of competitors
Fair treatment in search engines
sensitive feature = a content provider of a candidate item
↓
Information about who provides a candidate item can be ignored,
and providers are treated fairly
Fair treatment in recommendation
A hotel booking site should not abuse their position to recommend
hotels of its group company
[Bloomberg]

13. Exclusion of Unwanted Information
13
[TED Talk by Eli Pariser, http://www.filterbubble.com/]
sensitive feature = a political conviction of a friend candidate
↓
Information about whether a candidate is conservative or progressive
can be ignored in a recommendation process
Filter Bubble: To fit for Pariser’s preference, conservative people are
eliminated from his friend recommendation list in Facebook
Information unwanted by a user is excluded from recommendation

14. 14
Independence-Enhanced
Recommendation

15. Independence-Enhanced
Recommendation
15
[Kamishima+ 12, Kamishima+ 13, Kamishima+ 16, Kamishima+18]
Predicting Ratings: a task to predict a rating value that a user
would provide to an item
Dataset
Random variables: user X, item Y, rating R, sensitive feature S
Prediction Function
Dataset
Prediction Function
Standard Recommendation Independence-Enhanced
Recommendation
D = {(xi, yi, ri)} D = {(xi, yi, ri, si)}
Çr(x, y) Çr(x, y, s)

16. Regularization Approach
16
[Kamishima+ 12, Kamishima+ 13, Kamishima+18]
Objective Function independence parameter: control the balance
between independence and accuracy
independence term: a regularizer to constrain independence
The larger value indicates that recommendation outcomes and
sensitive values are more independent
Regularization Approach: Adopting a regularizer imposing a
constraint of independence while training a recommendation model
L2 regularizer
regularization
parameter
empirical loss
≥
D loss ri, Çr(xi, yi, si) * ⌘ indep(R, S) + 1
2
Ò⇥Ò2
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17. Independence Terms
17
Mutual Information with Histogram Models [Kamishima+ 12]
computationally inefficient
Mean Matching [Kamishima+ 13]
matching means of predicted ratings for distinct sensitive groups
improved computational efficiency, but considering only means
Mutual Information with Normal Distributions [Kamishima+ 18]
Distribution Matching with Bhattacharyya Distance [Kamishima+ 18]
These two terms can take both means and variances into account,
and are computationally efficient
* mean D(0)
* mean D(1) 2
*
⇠
* ln î
˘
Pr[rS=0] Pr[rS=1]dr
⇡
*
⇠
H (R) *
≥
s Pr[s] H (Rs)
⇡

18. Model-based Approach
18
Model-based Approach: a sensitive variable is added to a
recommendation model so that it satisfies an independence constraint
[Kamishima+ 16]
z
y
x r
s
z
y
x r
standard model independence-enhanced model
user
item
rating
latent variable sensitive
variable
independent
A model-based approach is inferior to a regularization approach
↑
Generative process of ratings are assumed to be probabilistic in this
model, but it is actually deterministic [Kamishima+ 18b]
[Hofmann 99]

19. Experiments: Accuracy vs Fairness
19
✽ Details of experimental conditions are shown in Sec. 4.2.1 and Fig. 2
We apply a regularization method with mutual information with
normal distributions to Movielens 1M with the Year sensitive feature
The changes of accuracy and independence measures according as
the enhancement of Independence
η
0
0.05
0.10
0.15
0.20
10
−2
10
−1
1 10
1
10
2
η
0.64
0.66
0.68
0.70
0.72
0.74
10
−2
10
−1
1 10
1
10
2
fair
accurate
independence-enhanced
standard
independence
-enhanced
standard
Recommendation independence could be successfully
enhanced by slightly sacrificing prediction accuracy
Accuracy
mean absolute error
Independence
Kolmogorov-Smirnov test statistics

20. Experiments: Means & Variances
20
η
3.0
3.2
3.4
3.6
3.8
4.0
0.4
0.6
0.8
1.0
1.2
1.4
10
−2
10
−1
1 10
1
10
2
η
3.0
3.2
3.4
3.6
3.8
4.0
0.4
0.6
0.8
1.0
1.2
1.4
10
−2
10
−1
1 10
1
10
2
✽ Details of experimental conditions are shown in Sec. 4.2.1 and Fig. 3
Comparison between our previous method, mean match, with our
latest method, mutual information with normal distributions
The changes of means and variances of predicted ratings according
as the enhancement of independence
differences of
standard deviations
differences of
means
mean matching
mutual information
with Gaussian models
Our previous method cannot control the variances of predicted
ratings, but our new method can
matchnot match

21. Conclusions
21
Contributions
We proposed a notion of recommendation independence
We have been developed methods for independence-enhanced
recommendation
Enhancement of recommendation independence have been
empirically examined
Our new independence terms could take variances of outcomes into
account
Future work
Recommendation independence for a find-good-items task
Sensitive features other than a binary type, such as a continuous
type
Other types of independence, such as equalized odds [Hardt+ 17, Yao+ 18]
In cases that are not point-estimation, such as Bayesian inference
Introduce conditional fairness or confounding variables

22. Additional Information
22
Program Codes (plan to open in March)
http://www.kamishima.net/iers/
Our Survey Slide of Fairness-Aware Data Mining
http://www.kamishima.net/archive/fadm.pdf
Acknowledgment
We gratefully acknowledge the valuable comments and suggestions of Dr. Paul
Resnick
We would like to thank the Grouplens research lab and Dr. Mohsen Jamali for
providing datasets
This work is supported by MEXT/JSPS KAKENHI Grant Number JP24500194,
JP15K00327, and JP16H02864

23. Bibliography I
Ò. Celma and P. Cano.
From hits to niches?: or how popular artists can bias music recommendation and
discovery.
In Proc. of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix
Prize Competition, 2008.
S. Forden.
Google said to face ultimatum from FTC in antitrust talks.
Bloomberg, Nov. 13 2012.
hhttp://bloom.bg/PPNEaSi.
A. Gunawardana and G. Shani.
A survey of accuracy evaluation metrics of recommendation tasks.
Journal of Machine Learning Research, 10:2935–2962, 2009.
M. Hardt, E. Price, and N. Srebro.
Equality of opportunity in supervised learning.
In Advances in Neural Information Processing Systems 29, 2016.
J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl.
Evaluating collaborative filtering recommender systems.
ACM Trans. on Information Systems, 22(1):5–53, 2004.
23

24. Bibliography II
T. Hofmann and J. Puzicha.
Latent class models for collaborative filtering.
In Proc. of the 16th Int’l Joint Conf. on Artificial Intelligence, pages 688–693, 1999.
M. Jamali and M. Ester.
A matrix factorization technique with trust propagation for recommendation in social
networks.
In Proc. of the 4th ACM Conf. on Recommender Systems, pages 135–142, 2010.
T. Kamishima and S. Akaho.
Considerations on recommendation independence for a find-good-items task.
In Workshop on Responsible Recommendation, 2017.
T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma.
Enhancement of the neutrality in recommendation.
In The 2nd Workshop on Human Decision Making in Recommender Systems, 2012.
T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma.
E ciency improvement of neutrality-enhanced recommendation.
In The 3rd Workshop on Human Decision Making in Recommender Systems, 2013.
T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma.
Model-based and actual independence for fairness-aware classification.
Data Mining and Knowledge Discovery, 32:258–286, 2018.
24

25. Bibliography III
T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma.
Recommendation independence.
In The 1st Conf. on Fairness, Accountability and Transparency, volume 81 of PMLR,
pages 187–201, 2018.
T. Kamishima, S. Akaho, H. Asoh, and I. Sato.
Model-based approaches for independence-enhanced recommendation.
In Proc. of the IEEE 16th Int’l Conf. on Data Mining Workshops, pages 860–867, 2016.
J. A. Konstan and J. Riedl.
Recommender systems: Collaborating in commerce and communities.
In Proc. of the SIGCHI Conf. on Human Factors in Computing Systems, Tutorial, 2003.
Y. Koren.
Factorization meets the neighborhood: A multifaceted collaborative filtering model.
In Proc. of the 14th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining,
pages 426–434, 2008.
E. Pariser.
The Filter Bubble: What The Internet Is Hiding From You.
Viking, 2011.
R. Salakhutdinov and A. Mnih.
Probabilistic matrix factorization.
In Advances in Neural Information Processing Systems 20, pages 1257–1264, 2008.
25

26. Bibliography IV
L. Sweeney.
Discrimination in online ad delivery.
Communications of the ACM, 56(5):44–54, 2013.
S. Yao and B. Huang.
Beyond parity: Fairness objectives for collaborative filtering.
In Advances in Neural Information Processing Systems 30, 2017.
C. N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen.
Improving recommendation lists through topic diversification.
In Proc. of the 14th Int’l Conf. on World Wide Web, pages 22–32, 2005.
26

27. 27
Extra Slides

28. Probabilistic Matrix Factorization
28
[Salakhutdinov 08, Koren 08]
Çr(x, y) = + bx + cy + pxqÒ
y
Probabilistic Matrix Factorization Model
predict a preference rating of an item y rated by a user x
well-performed and widely used
For a given training dataset, model parameters are learned by
minimizing the squared loss function with an L2 regularizer.
cross effect of
users and itemsglobal bias
user-dependent bias item-dependent bias
≥
D (ri * Çr(xi, yi))2
+ Ò⇥Ò2
Prediction Function
Objective Function
L2 regularizer
regularization parameter
squared loss function

29. Regularization Approach
29
[Kamishima+ 12, Kamishima+ 13, Kamishima+18]
≥
D ri * Çr(xi, yi, si)
2
* ⌘ indep(R, S) + Ò⇥Ò2
a prediction function is selected according to a sensitive value
sensitive feature
Çr(x, y, s) = (s)
+ b(s)
x + c(s)
y + p(s)
x q(s)
y
Ò
Prediction Function
Objective Function independence parameter: control the balance
between the independence and accuracy
independence term: a regularizer to constrain independence
The larger value indicates that ratings and sensitive values are more
independent
Matching means of predicted ratings for two sensitive values

30. Latent Class Model
30
[Hofmann 99]
Prediction:
z
y
x
r
Latent Class Model: A probabilistic model for collaborative filtering
A basic topic model, pLSA
extended so as to be able to deal with
ratings r given by users x to items y
Çr(x, y) = EPr[rx,y][level(r)]
=
≥
r Pr[rx, y] level(r)
the r-th rating value
A rating value can be predicted by the expectation of ratings
Model parameters can be learned by an EM algorithm
latent topic variable

31. Independence-Enhanced LCM
31
[Kamishima+ 16]
z
y
x
r
s
z
y
x
r
s
Independence-Enhancement by a Model-based Approach
A sensitive variable is embedded into the original LCM
A rating and a sensitive variable are mutually independent
A user, an item, and a rating are conditionally independent given Z
A type 2 model can more strictly enhance recommendation independence,
because in addition to X and Y, Z depends on a sensitive variable
Type 1 model Type 2 model

32. Ugly Duckling Theorem
32
[Watanabe 69]
Ugly Duckling Theorem
In classification, one must emphasize some features of objects and
must ignore the other features
It is impossible to make recommendation
that is independent from any sensitive features
a sensitive feature must be specified by a user
and other features are ignored
In a case of a Facebook example, A recommender system
enhances independent from a political conviction, but it is
allowed to make biased recommendations in terms of other
features, for example, the birthplace or age of friend candidates
Independence-enhanced Recommendation

33. For a Find-Good-Items Task
33
cross-entropy loss
Find Good Items: a task to find some items preferred by a user
↓
making a preference score independent, instead of a predicted rating
≥
D CE ri * Çr(xi, yi, si) * ⌘ indep(R, S) + Ò⇥Ò2
Çr(x, y, s) = sig (s)
+ b(s)
x + c(s)
y + p(s)
x q(s)
y
Ò
Preference Score: How strongly a user prefers an item
enhancing the independence between
a preference score and a sensitive feature
sigmoid function
[Kamishima+ 17]

34. Kolmogorov-Smirnov Statistic
34
The statistic of the two-sample Kolmogorov-Smirnov test
a nonparametric test for the equality of two distribution
↓
Evaluating the degree of independence
by measuring the equality between Pr[R | S=0] and Pr[R | S=1]
Kolmogorov-Smirnov statistic
the area between two empirical
cumulative distributions
Wikipedia

35. Preference Score vs Sensitive Feature
35
Observation 1: A preference score could be successfully made
independent from a sensitive feature
Observation 2: A ranking accuracy (AUC) did not worsen so
much by enhancement of the recommendation independence
This is was contrasted with the increase of a prediction error (MAE)
in a predicting ratings task
AUC
η0.70
0.75
0.80
0.85
0.90
10
−2
10
−1
1 10
1
10
2
KS
η0
0.05
0.10
0.15
0.20
10
−2
10
−1
1 10
1
10
2
Kolmogolov-Smirnov statistic
between Pr[R | S=0] and Pr[R | S=1]
ranking accuracy (AUC)
[Kamishima+ 17]

36. Relevance and Sensitive Feature
36
1.0 0.9 0.7 0.10.30.51.0
Recommending top-k items whose preference scores are the largest
sort according to their preference scores
select top-k items
irrelevant itemsrelevant items
check the independence from a relevance, not from a preference score
Observation 3: The relevance of items was not independent
from a sensitive feature for some values of k, in particular, small k
↓
A need for a new method that fits for a ranked item list
[Kamishima+ 17]

37. Popularity Bias
37
Popularity Bias
the tendency for popular items to be recommended more frequently
Flixster data
The degree popularity of an item is measured
by the number of users who rated the item
Short-head items are frequently and highly rated
[Jamali+ 10]
long-tail (bottom 99%)
share in ratings: 52.8%
mean rating: 3.53
short-head (top 1%)
share in ratings: 47.2%
mean rating: 3.71
sensitive feature = popularity of items
↓
Popularity bias can be corrected
[Celma 08]

38. Recommendation Diversity
38
[Ziegler+ 05]
Recommendation Diversity
Similar items are not recommended in a single list, to a single user,
to all users, or in a temporally successive lists
recommendation list
similar items
excluded
Diversity
Items that are similar in a
specified metric are excluded
from recommendation results
The mutual relations
among results
Independence
Information about a sensitive
feature is excluded from
recommendation results
The relations between
results and sensitive values

39. Diversity vs Independence
39
Diversity
Depending on the definition of
similarity measures
Independence
Depending on the specification of
sensitive feature
Similarity
A function of two items
Sensitive Feature
A function of a user-item pair
Because a sensitive feature depends on a user, neutrality can be
applicable for coping with users’ factor, such as, users’ gender or age,
which cannot be straightforwardly dealt by using diversity

40. Diversity vs Independence
40
short-head
long-tail
short-head
long-tail
standard recommendation diversified recommendation
Because a set of recommendations are diversified by abandoning
short-head items, predicted ratings are still biased
Prediction ratings themselves are unbiased by enhancing
recommendation independence

41. Privacy-Preserving Data Mining
41
recommendation results, R, and sensitive features, S,
are statistically independent
In a context of privacy-preservation
Even if the information about R is disclosed,
the information about S will not exposed
mutual information between a recommendation result, R,
and a sensitive feature, S, is zero
I(R; S) = 0
In particular, a notion of the t-closeness has strong connection