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 debiasing feedback loops in collaborative filtering recommender systems.

Artificial Intelligence (AI)-driven recommender systems have been gaining increasing ubiquity and influence in our daily lives, especially during time spent online on the World Wide Web or smart devices. The influence of recommender systems on who and what we can find and discover, our choices, and our behavior, has thus never been more concrete. AI can now predict and anticipate, with varying degrees of accuracy, the news article we will read, the music we will listen to, the movies we will watch, the transactions we will make, the restaurants we will eat in, the online courses we will be interested in, and the people we will connect with for various ends and purposes. For all these reasons, the automated predictions and recommendations made by AI can lead to influencing and changing human opinions, behavior, and decision making. When the AI predictions are biased, the influences can have unfair consequences on society, ranging from social polarization to the amplification of misinformation and hate speech. For instance, bias in recommender systems can affect the decision making and shift consumer behavior in an unfair way due to a phenomenon known as the feedback loop. The feedback loop is an inherent component of recommender systems because the latter are dynamic systems that involve continuous interactions with the users, whereby data collected to train a recommender system model is usually affected by the outputs of a previously trained model. This feedback loop is expected to affect the performance of the system. For instance, it can amplify initial bias in the data or model and can lead to other phenomena such as filter bubbles, polarization, and popularity bias. Up to now, it has been difficult to understand the dynamics of recommender system feedback loops, and equally challenging to evaluate the bias and filter bubbles emerging from recommender system models within such an iterative closed loop environment. In this dissertation, we study the feedback loop in the context of Collaborative Filtering (CF) recommender systems. CF systems comprise the leading family of recommender systems that rely mainly on mining the patterns of interaction between the users and items to train models that aim to predict future user interactions. Our research contributions target three aspects of recommendation, namely modeling, debiasing and evaluating feedback loops. Our research advances the state of the art in Fairness in Artificial Intelligence on several fronts: (1) We propose and validate a new theoretical model, based on Martingale differences, to model the recommender system feedback loop, and allow a better understanding of the dynamics of filter bubbles and user discovery. (2) We propose a Transformer-based deep learning architecture and algorithm to learn diverse representations for users and items in order to increase the diversity in the recommendations. Our evaluation experiments on real world datasets demonstrate that our transformer model recommends 14\% more diverse items and improves the novelty of the recommendation by more than 20\%. (3) We propose a new simulation and experimentation framework that allows studying and tracking the evolution of bias metrics in a feedback loop setting, for a variety of recommendation modeling algorithms. Our preliminary findings, using the new simulation framework show that recommender systems are deeply affected by the feedback loop, and that without an adequate debiasing or exploration strategy, this feedback loop limits the discovery of the user and increases the disparity in exposure between items that can be recommended. To help the research and practice community in studying recommender system fairness, all the tools developed to model, debias, and evaluate recommender systems are made available to the public as open source software libraries \footnote{https://github.com/samikhenissi/TheoretUserModeling}. (4) We propose a novel learnable dynamic debiasing strategy that learns an optimal rescaling parameter for the predicted rating and achieves a better trade-off between accuracy and debiasing. We focus on solving the popularity bias of the items and test our method using our proposed simulation framework and show the effectiveness of using a learnable debiasing degree to produce better results

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

Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers

Bidirectional Transformer architectures are state-of-the-art sequential recommendation models that use a bi-directional representation capacity based on the Cloze task, a.k.a. Masked Language Modeling. The latter aims to predict randomly masked items within the sequence. Because they assume that the true interacted item is the most relevant one, an exposure bias results, where non-interacted items with low exposure propensities are assumed to be irrelevant. The most common approach to mitigating exposure bias in recommendation has been Inverse Propensity Scoring (IPS), which consists of down-weighting the interacted predictions in the loss function in proportion to their propensities of exposure, yielding a theoretically unbiased learning. In this work, we argue and prove that IPS does not extend to sequential recommendation because it fails to account for the temporal nature of the problem. We then propose a novel propensity scoring mechanism, which can theoretically debias the Cloze task in sequential recommendation. Finally we empirically demonstrate the debiasing capabilities of our proposed approach and its robustness to the severity of exposure bias.Comment: 10 pages, 3 figures, Accepted at KDD '2

Debiasing the Human-Recommender System Feedback Loop in Collaborative Filtering

Recommender Systems (RSs) are widely used to help online users discover products, books, news, music, movies, courses, restaurants,etc. Because a traditional recommendation strategy always shows the most relevant items (thus with highest predicted rating), traditional RS’s are expected to make popular items become even more popular and non-popular items become even less popular which in turn further divides the haves (popular) from the have-nots (un-popular). Therefore, a major problem with RSs is that they may introduce biases affecting the exposure of items, thus creating a popularity divide of items during the feedback loop that occurs with users, and this may lead the RS to make increasingly biased recommendations over time. In this paper, we view the RS environment as a chain of events that are the result of interactions between users and the RS. Based on that, we propose several debiasing algorithms during this chain of events, and evaluate how these algorithms impact the predictive behavior of the RS, as well as trends in the popularity distribution of items over time. We also propose a novel blind-spot-aware matrix factorization (MF) algorithm to debias the RS. Results show that propensity matrix factorization achieved a certain level of debiasing of the RS while active learning combined with the propensity MF achieved a higher debiasing effect on recommendations

New Explainable Active Learning Approach for Recommender Systems

Introduction and Motivations Recommender Systems are intelligent programs that analyze patterns between items and users to predict the user’s taste. Objective Design an efficient Active Learning Strategy to increase the explainability and the accuracy of an “Explainable Matrix Factorization” model