Degenerate Feedback Loops in Recommender Systems

Machine learning is used extensively in recommender systems deployed in products. The decisions made by these systems can influence user beliefs and preferences which in turn affect the feedback the learning system receives - thus creating a feedback loop. This phenomenon can give rise to the so-called "echo chambers" or "filter bubbles" that have user and societal implications. In this paper, we provide a novel theoretical analysis that examines both the role of user dynamics and the behavior of recommender systems, disentangling the echo chamber from the filter bubble effect. In addition, we offer practical solutions to slow down system degeneracy. Our study contributes toward understanding and developing solutions to commonly cited issues in the complex temporal scenario, an area that is still largely unexplored

Beyond the Rating Matrix: Debiasing Implicit Feedback Loops in Collaborative Filtering

Implicit feedback collaborative filtering recommender systems suffer from exposure bias that corrupts performance and creates filter bubbles and echo chambers. Our study aims to provide a practical method that does not inherit any exposure bias from the data given the information about the user, the choice, and the choice set associated with each observation. We validated the model’s functionality and capability to reduce bias and compared it to baseline mitigation strategies by simulation. Our model inherited little to no bias, while the other approaches failed to mitigate all bias. To the best of our knowledge, we are first to identify a feasible approach to tackle exposure bias in recommender systems that does not require arbitrary parameter choices or large model extensions. With our findings, we encourage the recommender systems community to move away from rating-matrix-based towards discrete-choice-based models

Recommendation Systems: A Systematic Review

This article presents a comprehensive and objective systematic review of existing research on recommendation systems with regards to core theory, latest studies, various applications, current attitudes, and potential future applications. The research is mainly based on exploring professional peer-reviewed studies and articles and using their abstracts to create a comprehensive and unbiased review of existing research. The following search terms were used to identify articles and studies for the research: recommendation systems; recommender systems; core theory of recommender systems; current attitudes towards recommendation systems; latest studies on recommendation systems; applications of recommendation systems; potential studies on recommendation systems; and future potential applications of recommendation systems. The research also used the advanced search filter to locate recent studies for comparison by limiting the search by year to find studies published from 2021 onwards. Most literature on this area highlights the importance of recommendation systems in almost all aspects of modern life. Specifically, recommendation systems have become critical components in business, health care, education, marketing, and social networking domains. Additionally, most studies identified reinforcement of learning and deep learning techniques as significant developments in the field. These techniques form the backbone of most modern recommendation systems. The primary concern that could hinder further evolution systems is their consequent filter bubble effects which many studies showed to be problematic. Healthcare is a central area that shows tremendous potential for these systems. Although recommender systems have been implemented in this domain, there remains a lot of untapped potential that, if unleashed, could revolutionize medicine and healthcare. But the problems facing these systems have to be tackled first to establish trust. Keywords: Recommendation systems, Recommender systems, Deep learning, Reinforcement learning DOI: 10.7176/CEIS/13-4-04 Publication date:August 31st 202

An Offer you Cannot Refuse? Trends in the Coerciveness of Amazon Book Recommendations

Recommender systems can be a helpful tool for recommending content but they can also influence users' preferences. One sociological theory for this influence is that companies are incentivised to influence preferences to make users easier to predict and thus more profitable by making it harder to change preferences. This paper seeks to test that theory empirically. We use \textit{Barrier-to-Exit}, a metric for how difficult it is for users to change preferences, to analyse a large dataset of Amazon Book Ratings from 1998 to 2018. We focus the analysis on users who have changed preferences according to Barrier-to-Exit. To assess the growth of Barrier-to-Exit over time, we developed a linear mixed-effects model with crossed random effects for users and categories. Our findings indicate a highly significant growth of Barrier-to-Exit over time, suggesting that it has become more difficult for the analysed subset of users to change their preferences. However, it should be noted that these findings come with several statistical and methodological caveats including sample bias and construct validity issues related to Barrier-to-Exit. We discuss the strengths and limitations of our approach and its implications. Additionally, we highlight the challenges of creating context-sensitive and generalisable measures for complex socio-technical concepts such as "difficulty to change preferences." We conclude with a call for further research: to curb the potential threats of preference manipulation, we need more measures that allow us to compare commercial as well as non-commercial systems