Recommender systems play a key role in shaping modern web ecosystems. These systems alternate between (1) making recommendations, (2) collecting user responses to these recommendations, and (3) retraining the recommendation algorithm based on this feedback. During this process, the recommender influences the user behavioral data that is subsequently used to update the recommender itself, thus creating a feedback loop. Recent work has shown that feedback loops may compromise recommendation quality and homogenize user behavior, raising ethical and performance concerns around deploying recommender systems. To address these concerns, we propose the causal adjustment for feedback loops (CAFL) , an algorithm that uses causal inference to break feedback loops for any loss-minimizing recommendation algorithms. The key observation is that a recommender system does not suffer from feedback loops if it reasons about causal quantities, namely the intervention distributions of recommendations on user ratings. Moreover, we can calculate these intervention distributions from observational data by adjusting for the recommender system’s predictions of user preferences. Using simulated environments, we demonstrate that CAFL improves recommendation quality when compared to prior correction methods.

Recommender systems are inherently decision-making systems, taking actions that have consequences for the world around them.Some consequences might be desirable (for example, growing the user base for an online platform), others might be unintended (for example, amplifying inequality among item providers).In order to reason about these consequences, we need to resort to methods from the literature on causal inference.Whilst this research area has seen a growing interest in recent years, there is an abundance of open research questions from how we should model large-scale recommender systems in such causal frameworks, to what the limitations are for causal identifiability in general settings, and how we can properly handle confounding variables.The CONSEQUENCES workshop series aims to bring together researchers and practitioners who are interested in this research topic, and wish to help shape its future.

Recommender systems are inherently decision-making systems, taking actions that have consequences for the world around them. Some consequences might be desirable (for example, growing the user base for an online platform), others might be unintended (for example, amplifying inequality among item providers). In order to reason about these consequences, we need to resort to methods from the literature on causal inference. Whilst this research area has seen a growing interest in recent years, there is an abundance of open research questions from how we should model large-scale recommender systems in such causal frameworks, to what the limitations are for causal identifiability in general settings, and how we can properly handle confounding variables. The CONSEQUENCES workshop series aims to bring together researchers and practitioners who are interested in this research topic, and wish to help shape its future.

Recommender systems make algorithmic decisions about what will be shown to whom, billions of times every day across the web. These decisions have consequences that can often be far-reaching. Indeed, users that are exposed to certain items, might be convinced to explore interests that are novel to them. At the same time, exposure is often linked to economic incentives for the item producer, which platform-level metrics will be impacted by as well. Feedback loops in existing systems also imply that algorithmic decisions made by the model itself, have an impact on the data future model iterations will be trained and evaluated on.

Machine learning has allowed many systems that we interact with to improve performance and personalize. Recommender systems in particular are one of the largest users of machine learning in production environments that have improved performance of real-world systems. Learning in these interactive systems requires models that combine very diverse signals, including the logs of the interactive system (indicating if the intervention succeeded or failed) augmented with other data sources including: collaborative filtering, text, and image data. Bayesian inference is a compelling method to combine these diverse signals in a principled manner, but deployment of systems based on Bayesian principles remain challenging. The reward signal in the system logs is often uneven. Accurate estimation of reward is possible for exploiting actions, but often poor for other actions (exploration). Non-Bayesian methods such as inverse propensity score methods, the reinforce algorithm, and other heuristic-based approaches currently dominate practice. These commonly-used heuristics are often ineffective at leveraging diverse data. In contrast, Bayesian methods offer a principled, robust framework for learning from uneven signals and combining different types of information. Drawing upon the bandit and reinforcement learning community, in this workshop we will explore innovations in Bayesian inference for real world interactive systems, and consider advantages and limitations of the Bayesian approach.

The task of recommender systems is classically framed as a prediction of users’ preferences and users’ ratings. However, its spirit is to answer a counterfactual question: “What would the rating be if we ‘forced’ the user to watch the movie?” This is a question about an intervention, that is a causal inference question. The key challenge of this causal inference is unobserved confounders, variables that affect both which items the users decide to interact with and how they rate them. To this end, we develop an algorithm that leverages classical recommendation models for causal recommendation. Across simulated and real datasets, we demonstrate that the proposed algorithm is more robust to unobserved confounders and improves recommendation.