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Found 31 papers, 4 papers with code
Commonality in Recommender Systems: Evaluating Recommender Systems to Enhance Cultural Citizenship
After demonstrating that existing metrics do not center culture, we introduce a new metric, commonality, that measures the degree to which recommendations familiarize a given user population with specified categories of cultural content.
Recall, Robustness, and Lexicographic Evaluation
In this light, we reflect on the measurement of recall in rankings from a formal perspective.
A Survey of Diversification Techniques in Search and Recommendation
Diversifying search results is an important research topic in retrieval systems in order to satisfy both the various interests of customers and the equal market exposure of providers.
Striving for data-model efficiency: Identifying data externalities on group performance
Building trustworthy, effective, and responsible machine learning systems hinges on understanding how differences in training data and modeling decisions interact to impact predictive performance.
Retrieval Augmentation for T5 Re-ranker using External Sources
Retrieval augmentation has shown promising improvements in different tasks.
Analyzing the Effect of Sampling in GNNs on Individual Fairness
We evaluate two popular GNN methods: Graph Convolutional Network (GCN), which trains on the entire graph, and GraphSAGE, which uses probabilistic random walks to create subgraphs for mini-batch training, and assess the effects of sub-sampling on individual fairness.
Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural Citizenship
Recommender systems have become the dominant means of curating cultural content, significantly influencing the nature of individual cultural experience.
On Natural Language User Profiles for Transparent and Scrutable Recommendation
Natural interaction with recommendation and personalized search systems has received tremendous attention in recent years.
Retrieval-Enhanced Machine Learning
Although information access systems have long supported people in accomplishing a wide range of tasks, we propose broadening the scope of users of information access systems to include task-driven machines, such as machine learning models.
Joint Multisided Exposure Fairness for Recommendation
Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of the system.
Offline Retrieval Evaluation Without Evaluation Metrics
Offline evaluation of information retrieval and recommendation has traditionally focused on distilling the quality of a ranking into a scalar metric such as average precision or normalized discounted cumulative gain.
Exposing Query Identification for Search Transparency
Search systems control the exposure of ranked content to searchers.
Overview of the TREC 2020 Fair Ranking Track
This paper provides an overview of the NIST TREC 2020 Fair Ranking track.
Estimation of Fair Ranking Metrics with Incomplete Judgments
There is increasing attention to evaluating the fairness of search system ranking decisions.
Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization
FIDO learns to limit data collection based on an interpretation of data minimization tied to system performance.
The Benchmark Lottery
The world of empirical machine learning (ML) strongly relies on benchmarks in order to determine the relative effectiveness of different algorithms and methods.
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.
Multi-FR: A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation
To address these limitations, we propose a multi-objective optimization framework for fairness-aware recommendation, Multi-FR, that adaptively balances accuracy and fairness for various stakeholders with Pareto optimality guarantee.
Tip of the Tongue Known-Item Retrieval: A Case Study in Movie Identification
Using movie search as a case study, we explore the characteristics of questions posed by searchers in TOT states in a community question answering website.
On the Social and Technical Challenges of Web Search Autosuggestion Moderation
Past research shows that users benefit from systems that support them in their writing and exploration tasks.
Analyzing and Learning from User Interactions for Search Clarification
We also propose a model for learning representation for clarifying questions based on the user interaction data as implicit feedback.
Operationalizing the Legal Principle of Data Minimization for Personalization
Article 5(1)(c) of the European Union's General Data Protection Regulation (GDPR) requires that "personal data shall be [...] adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed (`data minimisation')".
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.
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.
Incorporating Query Term Independence Assumption for Efficient Retrieval and Ranking using Deep Neural Networks
Deep neural IR models, in contrast, compare the whole query to the document and are, therefore, typically employed only for late stage re-ranking.
Learning to Match Using Local and Distributed Representations of Text for Web Search
Models such as latent semantic analysis and those based on neural embeddings learn distributed representations of text, and match the query against the document in the latent semantic space.
The Social Dynamics of Language Change in Online Networks
Language change is a complex social phenomenon, revealing pathways of communication and sociocultural influence.
Query Expansion with Locally-Trained Word Embeddings
Continuous space word embeddings have received a great deal of attention in the natural language processing and machine learning communities for their ability to model term similarity and other relationships.
Real-Time Web Scale Event Summarization Using Sequential Decision Making
We present a system based on sequential decision making for the online summarization of massive document streams, such as those found on the web.
An Improved Discrete Bat Algorithm for Symmetric and Asymmetric Traveling Salesman Problems
Bat algorithm is a population metaheuristic proposed in 2010 which is based on the echolocation or bio-sonar characteristics of microbats.
Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains
We address both of these challenges with two complementary techniques: First, we develop a gradient-boosting style, non-parametric function approximator for learning on $Q$-function residuals.
