Recently, there has been a rising awareness that when machine learning (ML) algorithms are used to automate choices, they may treat/affect individuals unfairly, with legal, ethical, or economic consequences. Recommender systems are prominent examples of such ML systems that assist users in making high-stakes judgments. A common trend in the previous literature research on fairness in recommender systems is that the majority of works treat user and item fairness concerns separately, ignoring the fact that recommender systems operate in a two-sided marketplace. In this work, we present an optimization-based re-ranking approach that seamlessly integrates fairness constraints from both the consumer and producer-side in a joint objective framework. We demonstrate through large-scale experiments on 8 datasets that our proposed method is capable of improving both consumer and producer fairness without reducing overall recommendation quality, demonstrating the role algorithms may play in minimizing data biases.

1. CPFair: Personalized Consumer and Producer Fairness
Re-ranking for Recommender Systems
Mohammadmehdi Naghiaei1
, Hossein A. Rahmani2
, Yashar Deldjoo3
1
University of Southern California, naghiaei@usc.edu
2
University College London, h.rahmani@ucl.ac.uk
3
Polytechnic University of Bari, yashar.deldjoo@poliba.it
The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
Madrid | July 11-15, 2022

2. Table of contents
01
05
Conclusions
Introduction
Experiment Results
Motivation
04
02
Method
06
03
2

3. Introduction
01.
3

4. Biases in Recommender Systems
4
collection
recom
m
endation
learning
Recommender Engine
Data
User
Echo Chambers
Matthew Effect
Information Asymmetry
Potential Consequences of Unfairness
Chen, Jiawei, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. "Bias and debias in recommender system: A survey and future directions." arXiv preprint arXiv:2010.03240 (2020).
Yashar Deldjoo, Dietmar Jannach, Alejandro Bellogin, Alessandro Diffonzo, Dario Zanzonelli . "Fairness in Recommender Systems: Research Landscape and Future Directions.

5. Perspective and Studies in Fairness
5
Different Perspective in Fairness in RecSys
● User Fairness vs. Item Fairness
● Group Fairness vs. Individual Fairness
● Single-sided Fairness vs. Multi-sided Fairness
● Mitigating Biases Strategies:
○ [Pre, In, or Post]-processing
The Percentage of the Research Studied
Different Aspects of Fairness in RecSys1
1
The figures are based on 120 publications retrieved from DBLP using the keywords "fair/biased recommendation", "re-ranking", and "collaborative filtering".

6. Motivation
6
data analysis
SIGIR'22 | CPFair: Personalized Consumer and Producer Fairness

7. Motivation
7
algorithm analysis
The analysis of the recommendation quality of the fairness-unaware baseline algorithms on the two datasets
for both user and item groups
SIGIR'22 | CPFair: Personalized Consumer and Producer Fairness

8. Method
8
Dataset Rec. System Top-N Rec.
Cornac
Rating Matrix Rec. Lists
Solver Execution
(stakeholder)
Short-head item
Long-tail item
Active-user relevant item
Inactive-user relevant item
Stakeholders: N, C, P, CP
Re-ranked Top-N Rec.
Fair Rec. Lists
Re-ranking Approach

9. CPFair: Re-ranking Optimization Model
9
SIGIR'22 | CPFair: Personalized Consumer and Producer Fairness

10. Solver Modules
Filter-by-rating
k-core
Grouping
User groups
Item groups
Prefiltering
Loading
Ratings
Side Information
Base Recommendation
Model
Hyper-Parameter
Metrics
Precision
nDCG
Coverage
Novelty
Diversity
Bias
Fairness
Provider Fairness
Consumer Fairness
Optimization Solver
Recommendation Lists
Model Weights
Performance Tables
Output
Configuration File Data Modules Rec. Module Evaluation Modules Output Module
Data splitter
Consumer-Provider Fairness
Cornac Evaluation
rec. lists
rec. scores
user groups
item groups
Initial Preferences
Dataset
Input Layer
Optimization Layer
Result Layer
Proposed Approach
CPFair
https://github.com/rahmanidashti/CPFairRecSys

11. Experimental
Methodology
02.
11

12. Datasets
Dataset Users Items Interactions Sparsity Feedback Domain
MovieLens 943 1,349 99,287 92.19% Explicit Movie
Epinion 2,677 2,060 103,567 98.12% Explicit Opinion
LastFM 1,797 1,507 62,376 97.69% Implicit Music
BookCrossing 1,136 1,019 20,522 98.22% Explicit Book
Amazon Toy 2,170 1,733 32,852 99.12% Explicit eCommerce
Amazon Office 2,448 1,596 36,841 99.05% Explicit eCommerce
Gowalla 1,130 1,189 66,245 95.06% Implicit POI
Foursquare 1,568 1,461 42,678 98.13% Implicit POI
12
SIGIR'22 | CPFair: Personalized Consumer and Producer Fairness

13. The weights of each user and item latent
features are positive and modeled using the
Poisson distribution.
WMF assigns smaller weights to negative
samples and assumes that for two items their
latent features are independent.
NeuMF applies non-linear activation to train
the mapping between users and items
features that are concatenated from MLP and
MF layers.
VAECF introduces a generative model with
multinomial likelihood and uses Bayesian
inference for parameter estimation
Baselines
Models
PF
WMF
NeuMF
VAECF
13
SIGIR'22 | CPFair: Personalized Consumer and Producer Fairness

14. Evaluation Method
20 %
Train/Tune/Test Splits
Tune
14
Test
Train
10 %
70 %
Active
Inactive
95 %
5 %
Users Groups
80 %
20 %
Popular
short-head
Unpopular
long-tail
Items Groups
Interaction Data
Training Data
The number of interaction/level of activity
Training Data
The number of interactions they received
SIGIR'22 | CPFair: Personalized Consumer and Producer Fairness

15. Evaluation Metrics
Different Stakeholders
User relevance (nDCG) Item exposure
● All
● Active
● Inactive
● DCF (Act. - Inact.)
Both stakeholders
mCPF computes the weighted
average deviation of provider
fairness (DPF) and deviation of
consumer fairness (DCF).
15
● Novelty
● Coverage
● Short-head
● Long-tail
● DPF (Short. - Long.)
Consumer-Producer fairness evaluation (mCPF)
SIGIR'22 | CPFair: Personalized Consumer and Producer Fairness

16. Results
03.
16

17. 17
SIGIR'22 | CPFair: Personalized Consumer and Producer Fairness
Results

18. 18
SIGIR'22 | CPFair: Personalized Consumer and Producer Fairness

19. Conclusion and Future Work
19
● Studying the fairness-aware recommendation algorithms from both the user
and item perspectives
● Existing recommendation algorithms produce unfair recommendation
between different disadvantaged user and item groups
● Proposing a personalized CP-fairness constrained re-ranking method
● CPFair is able to mitigate the unfairness of both user and item groups while
maintaining the recommendation quality.
● Extensive experiments across 8 datasets indicate that our method can reduce
the unfair outcomes on beneficiary stakeholders, consumers and providers,
while improving the overall recommendation quality.

20. Thanks!
Do you have any questions?
Mohammadmehdi Naghiaei, University of Southern California, naghiaei@usc.edu
Hossein A. Rahmani, University College London, h.rahmani@ucl.ac.uk
Yashar Deldjoo, Polytechnic University of Bari, yashar.deldjoo@poliba.it
https://github.com/rahmanidashti/CPFairRecSys