=Paper= {{Paper |id=Vol-3012/OHARS2021-paper6 |storemode=property |title=PaRIS: Polarization-aware Recommender Interactive System |pdfUrl=https://ceur-ws.org/Vol-3012/OHARS2021-paper6.pdf |volume=Vol-3012 |authors=Mahsa Badami,Olfa Nasraoui |dblpUrl=https://dblp.org/rec/conf/recsys/BadamiN21 }} ==PaRIS: Polarization-aware Recommender Interactive System== https://ceur-ws.org/Vol-3012/OHARS2021-paper6.pdf

PaRIS: Polarization-aware Recommender Interactive
System
Mahsa Badami1 , Olfa Nasraoui1
1
Knowledge Discovery and Web Mining Lab, Computer Engineering and Computer Science Department, University of
Louisville, 132 Eastern Parkway, Louisville, Kentucky, USA, 40292

Abstract
One phenomenon that has been recently observed online is the emergence of polarization among users
on social networks, where the population gets divided in groups with opposite opinions. As recom-
mender system algorithms become more selective in filtering what users see and discover, one important
question arises: Could recommender system algorithms become more selective in filtering what users
see and discover? In this paper, we study this question and propose a new counter-polarization approach
for existing Matrix Factorization based recommender systems, that can be tuned by a user-controlled
counter-polarization parameter which serves like a voluntary user anti-polarization or discovery dial.

Keywords
Recommender System, Polarization, Algorithmic bias, Filter Bubble, Echo Chamber

1. Introduction
More than two-thirds of Americans get their news from online social media platforms 1 . At
the same time, social media services (e.g. Facebook) automatically filter and sort the order of
items on a user’s home feed, in many cases without an explicit request from the user to do
so [1]. As information filtering algorithms become more and more targeted and selective in
filtering what users see and discover, one important question arise: Could these algorithms filter
too much information? Despite the numerous benefits of personalized recommender systems,
narrow-minded over-specialization, resulting from positive feedback loops, can exacerbate
"echo chamber" and "filter bubble" by not expanding, or even restricting, the users’ exposure to
more diverse and non-obvious options [2, 3, 4, 5]. These two effects get even more extreme in
polarized environments leading to an even worse polarized environment [6, 7, 8].
The goal of our work is to expand the domain of recommended options and expand the
discovery potential for humans, and thus reducing the users’ chance of getting locked in
on an online platform. We aim to achieve this goal by designing a recommendation system
that not only recommends relevant items but also includes opposite views in case the user is
interested in discovering the opposite view. To do so, we propose a novel polarization-aware
recommender interactive system (PaRIS) which learns using a modified Non-Negative Matrix
OHARS’21: Second Workshop on Online Misinformation- and Harm-Aware Recommender Systems, October 2, 2021,
Amsterdam, Netherlands
" Mahsa.Badami@louisville.edu (M. Badami); Olfa.Nasraoui@louisville.edu (O. Nasraoui)
~ http://webmining.spd.louisville.edu (O. Nasraoui)
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings CEUR Workshop Proceedings (CEUR-WS.org)
http://ceur-ws.org
ISSN 1613-0073

1
https://www.theatlantic.com/technology/archive/2018/04/mark-zuckerberg-atlantic-exclusive/557489/

68
Factorization objective function. Our counter-polarization approach is modulated by a user-
controlled counter-polarization parameter which acts like a user discovery dial to give the user
the freedom to tune the severity of their filter bubbles as they wish.
The remainder of this paper is organized as follows. Section 3 presents our proposed methods
for handling polarization, followed by experiments in Section 4, and conclusions in Section 5.

2. Related Work
Research on polarization in recommender systems has emerged rapidly, in recent years, as an
important interdisciplinary topic [9, 10, 5], with efforts to formulate a richer understanding of
the potential characteristics of this Phenomenon and to decrease online polarization, especially
in recommender systems [10, 3, 11, 12].
Polarization has been investigated from a network perspective mainly using the social network
structure, the content and sentiment of discussions [13, 6], where as others studied polarization
based on the ratings provided by users on items, within the context of a recommender system
[13, 14]. Even though some researchers showed echo chambers in social media are somewhat
inevitable by design, other studies proposed strategies to mitigate such effects[15, 16, 17].
Badami et al. proposed a counter-polarization methodology for combating over-specialization
in polarized environments [16]. In another research, Tintarev el al. used visual explanations,
i.e., chord diagrams and bar charts [18] to address polarization.
Despite the reasonableness of prior works, most current work on polarization has relied
on textual content to detect sentiment and then polarization, or has been confined to specific
domains within the context of political (or other controversial domain) news and blogs. In this
paper, we are more interested in studying the emergence and aggravation of polarization as a
result of using collaborative filtering recommender systems.

3. Proposed Method
Our scope is within the context of classical collaborative filtering (CF) Recommendation algo-
rithms that learn latent factor models, specifically Non-negative Matrix Factorization (NMF), to
represent items and users based on a set of factors inferred from rating patterns.

3.1. Problem Definition
Following the polarization definition in [13], we define polarization-aware collaborative filtering
as follows:
Definition 1 - Polarization-aware collaborative filtering recommendation:
Given a set of ratings 𝑅 ∈ R𝑚×𝑛 collected from a set of users 𝑈 ∈ R1×𝑛 for a set of items
𝐼 ∈ R𝑚×1 , the problem of polarization-aware collaborative filtering recommendation (CF) can
be modeled by the triplet (𝑈, 𝐼, 𝑅), in a way that a recommender system should recommend a
ranked item set 𝑖1 , ..., 𝑖𝑡 ∈ 𝐼 according to 1) the relevance of the item to the user’s interest, and
2) the item’s polarization score. From definition 2, an instance from (𝑈, 𝐼, 𝑅) can be denoted by
(𝑢, 𝑖, 𝑟) which means that user 𝑢 rated item 𝑖 with value 𝑟.

69
3.2. Polarization-aware Recommender Interactive System - (PaRIS)
The general problem of Non-negative matrix Factorization (NMF) is to decompose a non-negative
data matrix 𝑅 with size 𝑚 × 𝑛 into two positive elements matrix factors 𝑃 and 𝑄, with size
𝑚 × 𝑘 and 𝑘 × 𝑛, respectively, such that 𝑘 ≪ min (𝑛, 𝑚) is a positive integer representing the
rank of matrices 𝑃 and 𝑄 [19]. Here, our goal is to design a recommendation system which not
only recommends relevant items but also includes opposite views in case the user is interested
to discover new items. A classical NMF predicts the overall rating 𝑟^𝑖𝑗 = 𝑝𝑖 .𝑞𝑗 by minimizing
||𝑟𝑖𝑗 − 𝑝𝑖 .𝑞𝑗 ||2 . However, the item rating alone is not able to fully consider the polarization
phenomenon. Hence, in order to estimate 𝑅 from (𝑈, 𝐼) such that the system considers both
relevance and polarization, we propose to minimize the objective function:
∑︁ ∑︁ (︁
min 𝒥𝑃 𝑎𝑅𝐼𝑆 = (1 − 𝜆𝑢 ) × ||𝑟𝑢𝑖 − 𝑝𝑢 𝑞𝑖 ||2 +
𝑝𝑢 , 𝑞 𝑖
𝑢 ∈𝑈 𝑖 ∈𝐼
)︁
′
𝜆𝑢 × ||𝑟𝑢𝑖 − 𝑝𝑢 𝑞𝑖 ||2
(1)
𝑔𝑖
′
𝑟𝑢𝑖 = 𝑟𝑢𝑖 − 𝜆𝑢 × (𝑟¯ + ) × Φ𝜆𝑖 𝑢 +𝑟𝑢𝑖 if 𝑟𝑢𝑖 ≥ 𝛿
𝑔𝑚𝑎𝑥
𝑔𝑖
′
𝑟𝑢𝑖 = 𝑟𝑢𝑖 + 𝜆𝑢 × (𝑟¯ − ) × Φ𝜆𝑖 𝑢 +𝑟𝑢𝑖 if 𝑟𝑢𝑖 < 𝛿
𝑔𝑚𝑎𝑥
where 𝜆𝑢 is user u’s discovery factor, Φ𝑖 is item i’s polarization score, computed using
the Polarization Detection Classifier proposed in [13], 𝑔𝑖 measures how extreme the different
viewpoints are, 𝛿 is a threshold that indicates which ratings are considered as liked versus
disliked. 𝑔𝑖 ∈ [0, 1] indicates the gap between the two rating extreme ranges for a polarized
item; in other words, it measures how polarized the user population’s ratings are for item 𝑖. We
use the gap 𝑔𝑖 as the difference between an item’s typical minimum rating when it is liked and
its typical maximum rating when it is disliked. 𝑔𝑚𝑎𝑥 is the difference between the maximum
and minimum rating that a typical user can provide for any item, using the system’s rating scale.
The more polarized a population gets, the higher 𝑔𝑖 gets. 𝛿 is a threshold that indicates which
ratings are considered as liked versus disliked. By minimizing the objective function in (1), we
estimate 𝑅 from (𝑈, 𝐼) such that the system considers both relevance and polarization, but to
different degrees. The first part of the optimization objective is the classical NMF optimization
criterion; while the second part is the counter-polarization component. The intuition behind
the second part is to bring a user and an item closer in the latent space, if the user is interested
in discovering more and the item happens to be polarized. The new incremental stochastic
Gradient Descent update equations at iteration 𝑡 + 1, after each new input 𝑟𝑢𝑖 , can be derived
as shown below, where 𝑒𝑢𝑖 (𝑒𝑢𝑖 ) are the ratings’ (depolarized ratings’) reconstruction errors.

𝑒𝑢𝑖 = 𝑟𝑢𝑖 − 𝑝𝑡𝑢 𝑞𝑖𝑡
(2)
𝑒′𝑢𝑖 = 𝑟𝑢𝑖
′
− 𝑝𝑡𝑢 𝑞𝑖𝑡
𝜕𝒥𝑃 𝑎𝑅𝐼𝑆
= 2(1 − 𝜆𝑢 ) × 𝑒𝑢𝑖 × (−𝑞𝑖 ) + 2𝜆𝑢 × 𝑒′𝑢𝑖 × (−𝑞𝑖 )
𝜕𝑝𝑢
(3)
𝜕𝒥𝑃 𝑎𝑅𝐼𝑆
= 2(1 − 𝜆𝑢 ) × 𝑒𝑢𝑖 × (−𝑝𝑢 ) + 2𝜆𝑢 × 𝑒′𝑢𝑖 × (−𝑝𝑢 )
𝜕𝑞𝑖

70
𝑝𝑡+1 = 𝑝𝑡𝑢 + 𝜂 2𝑞𝑖𝑡 ((1 − 𝜆𝑢 )𝑒𝑢𝑖 + 𝜆𝑢 × 𝑒′𝑢𝑖 )
(︀ )︀
𝑢
(4)
𝑞𝑗𝑡+1 = 𝑞𝑖𝑡 + 𝜂 2𝑝𝑡𝑢 ((1 − 𝜆𝑢 )𝑒𝑢𝑖 + 𝜆𝑢 × 𝑒′𝑢𝑖 )
(︀ )︀

The steps for the PaRIS algorithm are listed in Algorithm 1.

Algorithm 1 Polarization-aware Recommender Interactive System - ( PaRIS )

Input: initial user-item matrix (𝑅𝑡𝑟𝑎𝑖𝑛 ), 𝑓𝑃 𝐷𝐶 ( Polarization classifier function [13])
Output: final user-item matrix 𝑅

1: For each user 𝑢 ∈ 𝑈 :
2: Repeat While 𝑢 rates unrated items:
3: Update model 𝑃 , 𝑄 using input data 𝑅 by optimizing objective function (1)
4: with the parameter set of 𝜆𝑢 , Φ𝑖 , 𝑔𝑖
5: ¯ ← 𝑃𝑄
𝑅
6: Find 𝑆𝑢 which is the set of items sorted in descending order of predicted rating
7: Select the top 𝑘𝑡 items from S and recommend them to 𝑢
8: User picks an item 𝑖′ randomly and gives it rating 𝑟𝑢𝑖′
9: 𝑅 ← 𝑅 ⊙ 𝑟𝑢𝑖′ 𝑓 𝑜𝑟 𝑖′ ∈ 𝑆𝑢
10: where ⊙ is the I/O operator, meaning that user 𝑢 provides rating 𝑟𝑢𝑖′ for item 𝑖′

4. Experiments
For the purpose of evaluation, we resort to a simulation scenario in this preliminary work
similar to [16]. We evaluate the performance of our approach in terms of rating prediction
accuracy, using the Mean Squared Error (MSE) [19] and the Opposite View Hit Rate (OVHR)
ratio based on the ratio of the number of items from the opposite view to the total number of
recommended items [16]. We consider the following simple environment: Let 𝐺 = (𝑈, 𝐼, 𝑅)
be an environment where user 𝑢 ∈ 𝑈 can rate item 𝑖 ∈ 𝐼 with rating 𝑟𝑢𝑖 ∈ 𝑅 on a scale of 𝑥
to 𝑦. We generate a rating environment with 50 users and 200 items where items are evenly
divided in two opposite viewpoint sets (red items and blue items). Users are also divided into
two groups based on whether they like red or blue items. In order to make the environment
polarized, we assume that user 𝑢𝑎 ∈ 𝐺𝑟𝑜𝑢𝑝𝐴 likes red items more and user 𝑢𝑏 ∈ 𝐺𝑟𝑜𝑢𝑝𝐵
likes blue items more than red ones. Finally, we generated environment 𝐺 with different values
of polarization gaps and user discovery factors.
We start by showing some experiments that illustrate examples of how an Interactive Recom-
mender System (IRS) works in environment 𝐺. In all of the examples, we set the number of
factors in the latent space, 𝑘𝑓 , to 5 and we compute the list of top 𝑘𝑡 = 5 items to be recom-
mended to each user. The user will give a rating for only one of the selected items at a time. In
each iteration, we measure MSE from the training and testing phases. We also keep track of the
items that a user decided to reacted to by providing a rating.
Figure 1 shows traces from the interactive recommendation system for user 𝑢 ∈ 𝐺𝑟𝑜𝑢𝑝𝐴,

71
Figure 1: Traces of the Interactive Recommendation NMF-based RS in environment 𝐺 with different
polarization ratio and gap values, for user 𝑢𝑎 . Polarization increases acutely with Gap. The error drops
sharply in the more polarized case, making it easier for the Machine Learning model.

which means the user likes red items more than blue items. We generate environment 𝐺
considering that the gap 𝑔𝑖 is 2. Figure 1, upper row, shows that NMF is always going to
recommend red items, to which the user had previously shown more interest. Although the red
items are relevant, the user Red is trapped in a filter bubble that does not allow them to explore
any items from the opposite color/view. The second row shows the testing MSE decreases as
the user provides new ratings in each iteration; hence, there are fewer unrated items for the
user. Finally, the last row shows the NMF model’s objective or cost function’s convergence
when using gradient descent optimization, where each line (or colored thread) represents the
decrease of the objective function for an iteration of the interactive recommendation process.
Figure 2 shows the results of applying our proposed Polarization-aware Recommender Inter-
active System (PaRIS) in environment 𝐺 for user 𝑢𝑎 . As we can see, the user gets to see items
from a different color/viewpoint even in a very polarized environment. The middle row shows
the testing MSE error for user 𝑢𝑎 where there are some fluctuations in the testing MSE error
which is due to the modification in the main updating function of NMF. Finally, the last row
shows the contrast between the objective function evolution for varying polarization rates,
compared to non polarization. Furthermore the error converges to a smaller value for higher
polarization. We interpret this algorithmically, by the fact that the higher the polarization in

72
Figure 2: Traces of the Interactive Recommendation process with the polarization-aware recommender
system ( PaRIS ) in environment 𝐺 with different polarization ratio and gap values, for user 𝑢𝑎 who
had liked red items more than blue items. Polarization is reduced relative to NMF in Fig. 1 (top row).
As Gap increases, the prediction error drops according to two modes: one as sharp as the unmitigated
polarized case of NMF, and another decreasing more slowly like the unpolarized case at Gap = 0).

the ratings, the larger the gap (extreme likes and dislikes) between the items’ ratings in the
opposite viewpoints. Hence increased polarization leads to increased separation between the
opposite viewpoint ratings, which, very naturally makes learning the ratings an easier task
from a machine learning perspective.
We repeat the experiment with 𝑔𝑎𝑝 = 2 for two scenarios: (a) All users have the same 𝜆, i.e.
𝜆𝑢 = 𝑐 ∀𝑢 ∈ 𝑈 , where c is a constant ∈ [0, 1]. (b) User u has his/her own unique 𝜆, 𝜆𝑢 = 𝑐𝑢
for user u and 𝜆𝑢 = 0 ∀𝑢 ∈ 𝑈 − 𝑢, where 𝑐𝑢 ∈ [0, 1], is a user defined constant. Then, we
compute 𝑀 𝑆𝐸𝑡𝑒𝑠𝑡 , 𝑀 𝑆𝐸𝑡𝑟𝑎𝑖𝑛 and 𝑂𝑉 𝐻𝑅 in two ways: (a) 𝑂𝑉 𝐻𝑅𝑢 : the ratio of number of
items from the opposite view to what the user has picked from the recommendation list, (b)
𝑂𝑉 𝐻𝑅𝑡𝑘 : the ratio of number of items recommended to the user from an opposite view.
Table 1 shows that the higher the user-defined counter-polarization tuning parameter 𝜆, the
more they will be recommended items from the opposite view, again as desired by the user. Even
though the traditional NMF-based algorithm achieves good accuracy in rating prediction, it is
not able to recommend any item from the opposite view. In contrast, our proposed algorithm,

73
Table 1
Comparison of PaRIS with the classical NMF RS in terms of accuracy and opposite view ratio
(𝑂𝑉 𝐻𝑅𝑢 ,𝑂𝑉 𝐻𝑅𝑡𝑘 )
Opposite View Ratio 𝑀 𝑆𝐸𝑇 𝑟𝑎𝑖𝑛 𝑀 𝑆𝐸𝑇 𝑒𝑠𝑡
𝑂𝑉 𝐻𝑅𝑢 𝑂𝑉 𝐻𝑅𝑡𝑘
mean, std mean, std mean, std mean, std
Classic NMF 0.0 ± 0.00 22.02 ± 5.27 138.96 ± 12.55
𝜆𝑖 = 0.2 0.0% ± 0.00 0.0% ± 0.00 57.63 ± 14.35 223.31 ± 32.14
𝜆𝑖 = 0.5 0.0% ± 0.00 0.0% ± 0.00 146.51 ± 41.73 632.21 ± 57.73
Scenario (a)
𝜆𝑖 = 1.0 48.0% ± 0.17 32.0% ± 0.12 383.80 ± 108.52 2015.85 ± 167.46

PaRIS 𝜆𝑖 = 0.2 5.4% ± 0.073 4.9% ± 0.021 123.92 ± 36.76 813.01 ± 36.76
𝜆𝑖 = 0.5 6.0% ± 0.08 18.1% ± 0.21 124.46 ± 37.29 299.82 ± 76.01
Scenario (b)
𝜆𝑖 = 1.0 67.0% ± 0.24 68.0% ± 0.24 361.77 ± 102.74 1883.50 ± 237.83

PaRIS, recommends significantly more items (𝑝 ≤ 0.05) from the opposite view compared to
the baseline approach, for all the degrees of user-defined discovery factors.

5. Conclusions
We proposed a polarization-aware recommender system based on Non-negative Matrix Fac-
torization that succeeds to cover items from the opposite view after a few iterations and can
broaden the viewpoint spectrum even faster if the user is more interested in discovering items
from different viewpoints. Our work is limited by the simulation setting in the experiments,
since inducing polarization and testing different new strategies in real life has ethical risks
and legal ramifications. Future work should find a way to conduct tests on real users, after
mitigating the risks involved.

6. Acknowledgments
This work was supported by the National Science Foundation through grant IIS-154998.

References
[1] R. Baeza-Yates, Data and algorithmic bias in the web, in: Proceedings of the 8th ACM
Conference on Web Science, ACM, 2016, pp. 1–1.
[2] J. Sanz-Cruzado, P. Castells, Enhancing structural diversity in social networks by recom-
mending weak ties, in: Proceedings of the 12th ACM conference on recommender systems,
2018, pp. 233–241.
[3] E. Bakshy, S. Messing, L. A. Adamic, Exposure to ideologically diverse news and opinion
on facebook, Science 348 (2015) 1130–1132.

74
[4] E. Pariser, The filter bubble: How the new personalized web is changing what we read and
how we think, Penguin, 2011.
[5] Y. Ge, S. Zhao, H. Zhou, C. Pei, F. Sun, W. Ou, Y. Zhang, Understanding echo chambers in
e-commerce recommender systems, in: Proceedings of the 43rd international ACM SIGIR
conference on research and development in information retrieval, 2020, pp. 2261–2270.
[6] U. Chitra, C. Musco, Analyzing the impact of filter bubbles on social network polarization,
in: Proceedings of the 13th International Conference on Web Search and Data Mining,
2020, pp. 115–123.
[7] O. Nasraoui, P. Shafto, Human-algorithm interaction biases in the big data cycle: A markov
chain iterated learning framework, CoRR abs/1608.07895 (2016). URL: http://arxiv.org/abs/
1608.07895.
[8] J. Möller, D. Trilling, N. Helberger, B. van Es, Do not blame it on the algorithm: an
empirical assessment of multiple recommender systems and their impact on content
diversity, Information, Communication & Society 21 (2018) 959–977.
[9] P. Dandekar, A. Goel, D. T. Lee, Biased assimilation, homophily, and the dynamics of
polarization, Proceedings of the National Academy of Sciences 110 (2013) 5791–5796.
[10] K. Garimella, G. D. F. Morales, A. Gionis, M. Mathioudakis, Balancing opposing views to
reduce controversy, arXiv preprint arXiv:1611.00172 (2016).
[11] J. N. Cohen, Exploring echo-systems: how algorithms shape immersive media environ-
ments., Journal of Media Literacy Education 10 (2018) 139–151.
[12] A.-A. Stoica, A. Chaintreau, Hegemony in social media and the effect of recommendations,
in: Companion Proceedings of The 2019 World Wide Web Conference, 2019, pp. 575–580.
[13] M. Badami, O. Nasraoui, W. Sun, P. Shafto, Detecting polarization in ratings: An automated
pipeline and a preliminary quantification on several benchmark data sets, in: Big Data
(Big Data), 2017 IEEE International Conference on, IEEE, 2017.
[14] P. Victor, C. Cornelis, M. De Cock, A. Teredesai, A comparative analysis of trust-enhanced
recommenders for controversial items., in: ICWSM, 2009.
[15] A. Antikacioglu, R. Ravi, Post processing recommender systems for diversity, in: Proceed-
ings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and
Data Mining, 2017, pp. 707–716.
[16] M. Badami, O. Nasraoui, P. Shafto, Prcp: Pre-recommendation counter-polarization., in:
KDIR, 2018, pp. 280–287.
[17] M. Gao, H. J. Do, W.-T. Fu, Burst your bubble! an intelligent system for improving
awareness of diverse social opinions, in: 23rd International Conference on Intelligent User
Interfaces, 2018, pp. 371–383.
[18] N. Tintarev, S. Rostami, B. Smyth, Knowing the unknown: visualising consumption blind-
spots in recommender systems, in: Proceedings of the 33rd Annual ACM Symposium on
Applied Computing, 2018, pp. 1396–1399.
[19] Y. Koren, R. Bell, C. Volinsky, Matrix factorization techniques for recommender systems,
Computer 42 (2009).