Open AccessDOI
The Stereotyping Problem in Collaboratively Filtered Recommender Systems
TLDR
In this paper, the authors introduce the notion of joint accessibility, which measures the extent to which a set of items can jointly be accessed by users, and provide theoretical necessary and sufficient conditions when joint accessibility is violated.Abstract:
Recommender systems play a crucial role in mediating our access to online information. We show that such algorithms induce a particular kind of stereotyping: if preferences for a set of items are anti-correlated in the general user population, then those items may not be recommended together to a user, regardless of that user’s preferences and rating history. First, we introduce a notion of joint accessibility, which measures the extent to which a set of items can jointly be accessed by users. We then study joint accessibility under the standard factorization-based collaborative filtering framework, and provide theoretical necessary and sufficient conditions when joint accessibility is violated. Moreover, we show that these conditions can easily be violated when the users are represented by a single feature vector. To improve joint accessibility, we further propose an alternative modelling fix, which is designed to capture the diverse multiple interests of each user using a multi-vector representation. We conduct extensive experiments on real and simulated datasets, demonstrating the stereotyping problem with standard single-vector matrix factorization models.read more
Citations
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Proceedings ArticleDOI
Fair ranking: a critical review, challenges, and future directions
TL;DR: In this paper , the authors provide a critical overview of this literature, detailing the often context-specific concerns that such approaches miss: the gap between high ranking placements and true provider utility, spillovers and compounding effects over time, induced strategic incentives, and the effect of statistical uncertainty.
Journal ArticleDOI
Supply-Side Equilibria in Recommender Systems
TL;DR: This paper investigates the supply-side equilibria in content recommender systems, model users and content as D -dimensional vectors, and shows that producers can achieve positive profit at equilibrium, which is typically impossible under perfect competition.
Proceedings ArticleDOI
The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm
TL;DR: This paper focuses on a challenging scenario where a user has multiple categories of interests, and proposes a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user.
Book ChapterDOI
Individual Fairness for Social Media Influencers
TL;DR: In this article , the authors extend prior work by looking beyond averages to assess the fairness of the tie formation process and investigate the importance of exploratory recommendations for achieving fair outcomes, showing that non-exploratory recommendations converge fast but usually lead to unfair outcomes.
Learning to Suggest Breaks: Sustainable Optimization of Long-Term User Engagement
Eden Saig,Nir Rosenfeld +1 more
TL;DR: In this article , the authors study the role of breaks in recommendation and propose a framework for learning optimal breaking policies that promote and sustain long-term engagement, based on the notion that recommendation dynamics are susceptible to both positive and negative feedback.
References
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Journal ArticleDOI
Matrix Factorization Techniques for Recommender Systems
TL;DR: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.
Journal ArticleDOI
Evaluating collaborative filtering recommender systems
TL;DR: The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.
Journal ArticleDOI
The MovieLens Datasets: History and Context
TL;DR: The history of MovieLens and the MovieLens datasets is documents, including a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization, and best practices and limitations of using the Movie Lens datasets in new research are documented.
Proceedings ArticleDOI
Collaborative Filtering for Implicit Feedback Datasets
TL;DR: This work identifies unique properties of implicit feedback datasets and proposes treating the data as indication of positive and negative preference associated with vastly varying confidence levels, which leads to a factor model which is especially tailored for implicit feedback recommenders.
Proceedings ArticleDOI
Recommender systems in e-commerce
TL;DR: An explanation of howRecommender systems help E-commerce sites increase sales, and a taxonomy of recommender systems, including the interfaces they present to customers, the technologies used to create the recommendations, and the inputs they need from customers.