Search Results for author:
Found 23 papers, 3 papers with code
Exploring Author Gender in Book Rating and Recommendation
Collaborative filtering algorithms find useful patterns in rating and consumption data and exploit these patterns to guide users to good items.
LensKit for Python: Next-Generation Software for Recommender System Experiments
LensKit is an open-source toolkit for building, researching, and learning about recommender systems.
Recommender Systems Notation: Proposed Common Notation for Teaching and Research
In the course of years of teaching and research on recommender systems, we have seen the val-ue in adopting a consistent notation across our work.
Proceedings of FACTS-IR 2019
The proceedings list for the program of FACTS-IR 2019, the Workshop on Fairness, Accountability, Confidentiality, Transparency, and Safety in Information Retrieval held at SIGIR 2019.
Estimating Error and Bias in Offline Evaluation Results
Offline evaluations of recommender systems attempt to estimate users' satisfaction with recommendations using static data from prior user interactions.
Overview of the TREC 2019 Fair Ranking Track
The goal of the TREC Fair Ranking track was to develop a benchmark for evaluating retrieval systems in terms of fairness to different content providers in addition to classic notions of relevance.
Evaluating Stochastic Rankings with Expected Exposure
We introduce the concept of \emph{expected exposure} as the average attention ranked items receive from users over repeated samples of the same query.
Comparing Fair Ranking Metrics
Ranked lists are frequently used by information retrieval (IR) systems to present results believed to be relevant to the users information need.
Fairness in Information Access Systems
Recommendation, information retrieval, and other information access systems pose unique challenges for investigating and applying the fairness and non-discrimination concepts that have been developed for studying other machine learning systems.
Pink for Princesses, Blue for Superheroes: The Need to Examine Gender Stereotypes in Kid's Products in Search and Recommendations
In this position paper, we argue for the need to investigate if and how gender stereotypes manifest in search and recommender systems. As a starting point, we particularly focus on how these systems may propagate and reinforce gender stereotypes through their results in learning environments, a context where teachers and children in their formative stage regularly interact with these systems.
Overview of the TREC 2020 Fair Ranking Track
This paper provides an overview of the NIST TREC 2020 Fair Ranking track.
Statistical Inference: The Missing Piece of RecSys Experiment Reliability Discourse
In this paper, we argue that the use of statistical inference is a key component of the evaluation process that has not been given sufficient attention.
Multiversal Simulacra: Understanding Hypotheticals and Possible Worlds Through Simulation
Recommender systems research is concerned with many aspects of recommender system behavior and effects than simply its effectiveness, and simulation can be a powerful tool for uncovering these effects.
Fire Dragon and Unicorn Princess; Gender Stereotypes and Children's Products in Search Engine Responses
Search engines in e-commerce settings allow users to search, browse, and select items from a wide range of products available online including children's items.
Matching Consumer Fairness Objectives & Strategies for RecSys
The last several years have brought a growing body of work on ensuring that recommender systems are in some sense consumer-fair -- that is, they provide comparable quality of service, accuracy of representation, and other effects to their users.
Much Ado About Gender: Current Practices and Future Recommendations for Appropriate Gender-Aware Information Access
Information access research (and development) sometimes makes use of gender, whether to report on the demographics of participants in a user study, as inputs to personalized results or recommendations, or to make systems gender-fair, amongst other purposes.
Overview of the TREC 2022 Fair Ranking Track
The 2022 Fair Ranking track aimed to ensure that documents that are about, or somehow represent, certain protected characteristics receive a fair exposure to the Wikipedia editors, so that the documents have an fair opportunity of being improved and, therefore, be well-represented in Wikipedia.
Overview of the TREC 2021 Fair Ranking Track
The 2021 Fair Ranking track aimed to ensure that documents that are about, or somehow represent, certain protected characteristics receive a fair exposure to the Wikipedia editors, so that the documents have an fair opportunity of being improved and, therefore, be well-represented in Wikipedia.
Patterns of gender-specializing query reformulation
There are many ways a query, the search results, and a demographic attribute such as gender may relate, leading us to hypothesize different causes for these reformulation patterns, such as under-representation on the original result page or based on the linguistic theory of markedness.
Inference at Scale Significance Testing for Large Search and Recommendation Experiments
However, these studies are focused on TREC-style experiments, which typically have fewer than 100 topics.
Distributionally-Informed Recommender System Evaluation
Current practice for evaluating recommender systems typically focuses on point estimates of user-oriented effectiveness metrics or business metrics, sometimes combined with additional metrics for considerations such as diversity and novelty.
Towards Measuring Fairness in Grid Layout in Recommender Systems
We examine how fairness scores change with different ranking layouts to yield insights into (1) the consistency of fair ranking measurements across layouts; (2) whether rankings optimized for fairness in a linear ranking remain fair when the results are displayed in a grid; and (3) the impact of column reduction approaches to support different device geometries on fairness measurement.
Candidate Set Sampling for Evaluating Top-N Recommendation
The strategy for selecting candidate sets -- the set of items that the recommendation system is expected to rank for each user -- is an important decision in carrying out an offline top-$N$ recommender system evaluation.
