7 research outputs found
Modeling and counteracting exposure bias in recommender systems.
Recommender systems are becoming widely used in everyday life. They use machine learning algorithms which learn to predict our preferences and thus influence our choices among a staggering array of options online, such as movies, books, products, and even news articles. Thus what we discover and see online, and consequently our opinions and decisions, are becoming increasingly affected by automated predictions made by learning machines. Similarly, the predictive accuracy of these learning machines heavily depends on the feedback data, such as ratings and clicks, that we provide them. This mutual influence can lead to closed-loop interactions that may cause unknown biases which can be exacerbated after several iterations of machine learning predictions and user feedback. Such machine-caused biases risk leading to undesirable social effects such as polarization, unfairness, and filter bubbles. In this research, we aim to study the bias inherent in widely used recommendation strategies such as matrix factorization and its impact on the diversity of the recommendations. We also aim to develop probabilistic models of the bias that is borne from the interaction between the user and the recommender system and to develop debiasing strategies for these systems. We present a theoretical framework that can model the behavioral process of the user by considering item exposure before user interaction with the model. We also track diversity metrics to measure the bias that is generated in recommender systems, and thus study their effect throughout the iterations. Finally, we try to mitigate the recommendation system bias by engineering solutions for several state of the art recommender system models. Our results show that recommender systems are biased and depend on the prior exposure of the user. We also show that the studied bias iteratively decreases diversity in the output recommendations. Our debiasing method demonstrates the need for alternative recommendation strategies that take into account the exposure process in order to reduce bias. Our research findings show the importance of understanding the nature of and dealing with bias in machine learning models such as recommender systems that interact directly with humans, and are thus causing an increasing influence on human discovery and decision making
Modeling and Counteracting Exposure Bias in Recommender Systems
What we discover and see online, and consequently our opinions and decisions,
are becoming increasingly affected by automated machine learned predictions.
Similarly, the predictive accuracy of learning machines heavily depends on the
feedback data that we provide them. This mutual influence can lead to
closed-loop interactions that may cause unknown biases which can be exacerbated
after several iterations of machine learning predictions and user feedback.
Machine-caused biases risk leading to undesirable social effects ranging from
polarization to unfairness and filter bubbles.
In this paper, we study the bias inherent in widely used recommendation
strategies such as matrix factorization. Then we model the exposure that is
borne from the interaction between the user and the recommender system and
propose new debiasing strategies for these systems.
Finally, we try to mitigate the recommendation system bias by engineering
solutions for several state of the art recommender system models.
Our results show that recommender systems are biased and depend on the prior
exposure of the user. We also show that the studied bias iteratively decreases
diversity in the output recommendations. Our debiasing method demonstrates the
need for alternative recommendation strategies that take into account the
exposure process in order to reduce bias.
Our research findings show the importance of understanding the nature of and
dealing with bias in machine learning models such as recommender systems that
interact directly with humans, and are thus causing an increasing influence on
human discovery and decision makingComment: 9 figures and one table. The paper has 5 page
Fairness of Exposure in Dynamic Recommendation
Exposure bias is a well-known issue in recommender systems where the exposure
is not fairly distributed among items in the recommendation results. This is
especially problematic when bias is amplified over time as a few items (e.g.,
popular ones) are repeatedly over-represented in recommendation lists and
users' interactions with those items will amplify bias towards those items over
time resulting in a feedback loop. This issue has been extensively studied in
the literature in static recommendation environment where a single round of
recommendation result is processed to improve the exposure fairness. However,
less work has been done on addressing exposure bias in a dynamic recommendation
setting where the system is operating over time, the recommendation model and
the input data are dynamically updated with ongoing user feedback on
recommended items at each round. In this paper, we study exposure bias in a
dynamic recommendation setting. Our goal is to show that existing bias
mitigation methods that are designed to operate in a static recommendation
setting are unable to satisfy fairness of exposure for items in long run. In
particular, we empirically study one of these methods and show that repeatedly
applying this method fails to fairly distribute exposure among items in long
run. To address this limitation, we show how this method can be adapted to
effectively operate in a dynamic recommendation setting and achieve exposure
fairness for items in long run. Experiments on a real-world dataset confirm
that our solution is superior in achieving long-term exposure fairness for the
items while maintaining the recommendation accuracy
Label Denoising through Cross-Model Agreement
Learning from corrupted labels is very common in real-world machine-learning
applications. Memorizing such noisy labels could affect the learning of the
model, leading to sub-optimal performances. In this work, we propose a novel
framework to learn robust machine-learning models from noisy labels. Through an
empirical study, we find that different models make relatively similar
predictions on clean examples, while the predictions on noisy examples vary
much more across different models. Motivated by this observation, we propose
\em denoising with cross-model agreement \em (DeCA) which aims to minimize the
KL-divergence between the true label distributions parameterized by two machine
learning models while maximizing the likelihood of data observation. We employ
the proposed DeCA on both the binary label scenario and the multiple label
scenario. For the binary label scenario, we select implicit feedback
recommendation as the downstream task and conduct experiments with four
state-of-the-art recommendation models on four datasets. For the multiple-label
scenario, the downstream application is image classification on two benchmark
datasets. Experimental results demonstrate that the proposed methods
significantly improve the model performance compared with normal training and
other denoising methods on both binary and multiple-label scenarios.Comment: arXiv admin note: substantial text overlap with arXiv:2105.0960
Bias in short-video recommender systems: user-centric evaluation on TikTok
Recommender systems enable users to navigate in the sea of mass information. TikTok, one of the fastest-growing short-video social platforms, offers countless videos that are curated according to users’ interests by the recommendation engine of For You page. However, the bias in recommendation brought on by the nature of the algorithm impact user experience in a number of aspects. In order to identify the mechanism and bias in the TikTok recommendation system, this study conducts two user-centric methods of data collection: semi-structured interview and walkthrough evaluation. This study aims to analyze the algorithm and bias of recommendation while exploring the user experience of using TikTok and how different types of bias affect their experience. Upon the analysis of data, the findings indicate that popularity bias and exposure bias exist in the system, and the user experience is influenced due to the bias.Master of Science in Information Scienc