136 research outputs found

    Debiasing the Human-Recommender System Feedback Loop in Collaborative Filtering

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    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

    Modeling and debiasing feedback loops in collaborative filtering recommender systems.

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    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

    Studying and handling iterated algorithmic biases in human and machine learning interaction.

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    Algorithmic bias consists of biased predictions born from ingesting unchecked information, such as biased samples and biased labels. Furthermore, the interaction between people and algorithms can exacerbate bias such that neither the human nor the algorithms receive unbiased data. Thus, algorithmic bias can be introduced not only before and after the machine learning process but sometimes also in the middle of the learning process. With a handful of exceptions, only a few categories of bias have been studied in Machine Learning, and there are few, if any, studies of the impact of bias on both human behavior and algorithm performance. Although most research treats algorithmic bias as a static factor, we argue that algorithmic bias interacts with humans in an iterative manner producing a long-term effect on algorithms\u27 performance. Recommender systems involve the natural interaction between humans and machine learning algorithms that may introduce bias over time during a continuous feedback loop, leading to increasingly biased recommendations. Therefore, in this work, we view a Recommender system environment as generating a continuous chain of events as a result of the interactions between users and the recommender system outputs over time. For this purpose, In the first part of this dissertation, we employ an iterated-learning framework that is inspired from human language evolution to study the impact of interaction between machine learning algorithms and humans. Specifically, our goal is to study the impact of the interaction between two sources of bias: the process by which people select information to label (human action); and the process by which an algorithm selects the subset of information to present to people (iterated algorithmic bias mode). Specifically, we investigate three forms of iterated algorithmic bias (i.e. personalization filter, active learning, and a random baseline) and how they affect the behavior of machine learning algorithms. Our controlled experiments which simulate content-based filters, demonstrate that the three iterated bias modes, initial training data class imbalance, and human action affect the models learned by machine learning algorithms. We also found that iterated filter bias, which is prominent in personalized user interfaces, can lead to increased inequality in estimated relevance and to a limited human ability to discover relevant data. In the second part of this dissertation work, we focus on collaborative filtering recommender systems which suffer from additional biases due to the popularity of certain items, which when coupled with the iterated bias emerging from the feedback loop between human and algorithms, leads to an increased divide between the popular items (the haves) and the unpopular items (the have-nots). We thus propose several debiasing algorithms, including a novel blind spot aware matrix factorization algorithm, and evaluate how our proposed algorithms impact both prediction accuracy and the trends of increase or decrease in the inequality of the popularity distribution of items over time. Our findings indicate that the relevance blind spot (items from the testing set whose predicted relevance probability is less than 0.5) amounted to 4\% of all relevant items when using a content-based filter that predicts relevant items. A similar simulation using a real-life rating data set found that the same filter resulted in a blind spot size of 75\% of the relevant testing set. In the case of collaborative filtering for synthetic rating data, and when using 20 latent factors, Conventional Matrix Factorization resulted in a ranking-based blind spot (items whose predicted ratings are below 90\% of the maximum predicted ratings) ranging between 95\% and 99\% of all items on average. Both Propensity-based Matrix Factorization methods resulted in blind spots consisting of between 94\% and 96\% of all items; while the Blind spot aware Matrix Factorization resulted in a ranking-based blind spot with around 90\% to 94\% of all items. For a semi-synthetic data (a real rating data completed with Matrix Factorization), Matrix Factorization using 20 latent factors, resulted in a ranking-based blind spot containing between 95\% and 99\% of all items. Popularity-based and Poisson based propensity-based Matrix Factorization resulted in a ranking-based blind spot with between 96\% and 97\% if all items; while the blind spot aware Matrix Factorization resulted in a ranking-based blind spot with between 92\% and 96\% of all items. Considering that recommender systems are typically used as gateways that filter massive amounts of information (in the millions) for relevance, these blind spot percentage result differences (every 1\% amounts to tens of thousands of items or options) show that debiasing these systems can have significant repercussions on the amount of information and the space of options that can be discovered by humans who interact with algorithmic filters

    Modeling and counteracting exposure bias in recommender systems.

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    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

    Biases in scholarly recommender systems: impact, prevalence, and mitigation

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    We create a simulated financial market and examine the effect of different levels of active and passive investment on fundamental market efficiency. In our simulated market, active, passive, and random investors interact with each other through issuing orders. Active and passive investors select their portfolio weights by optimizing Markowitz-based utility functions. We find that higher fractions of active investment within a market lead to an increased fundamental market efficiency. The marginal increase in fundamental market efficiency per additional active investor is lower in markets with higher levels of active investment. Furthermore, we find that a large fraction of passive investors within a market may facilitate technical price bubbles, resulting in market failure. By examining the effect of specific parameters on market outcomes, we find that that lower transaction costs, lower individual forecasting errors of active investors, and less restrictive portfolio constraints tend to increase fundamental market efficiency in the market

    Modeling and Counteracting Exposure Bias in Recommender Systems

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    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

    A Survey on Fairness-aware Recommender Systems

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    As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, which makes them vital and indispensable to human society in the information era. However, as people become more dependent on them, recent studies show that recommender systems potentially own unintentional impacts on society and individuals because of their unfairness (e.g., gender discrimination in job recommendations). To develop trustworthy services, it is crucial to devise fairness-aware recommender systems that can mitigate these bias issues. In this survey, we summarise existing methodologies and practices of fairness in recommender systems. Firstly, we present concepts of fairness in different recommendation scenarios, comprehensively categorize current advances, and introduce typical methods to promote fairness in different stages of recommender systems. Next, after introducing datasets and evaluation metrics applied to assess the fairness of recommender systems, we will delve into the significant influence that fairness-aware recommender systems exert on real-world industrial applications. Subsequently, we highlight the connection between fairness and other principles of trustworthy recommender systems, aiming to consider trustworthiness principles holistically while advocating for fairness. Finally, we summarize this review, spotlighting promising opportunities in comprehending concepts, frameworks, the balance between accuracy and fairness, and the ties with trustworthiness, with the ultimate goal of fostering the development of fairness-aware recommender systems.Comment: 27 pages, 9 figure

    How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility

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    Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering decisions. These systems are often evaluated or trained with data from users already exposed to algorithmic recommendations; this creates a pernicious feedback loop. Using simulations, we demonstrate how using data confounded in this way homogenizes user behavior without increasing utility

    Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers

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    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
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