Search Results for author: Fernando Diaz

Found 31 papers, 4 papers with code

Commonality in Recommender Systems: Evaluating Recommender Systems to Enhance Cultural Citizenship

no code implementations22 Feb 2023 Andres Ferraro, Gustavo Ferreira, Fernando Diaz, Georgina Born

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.

Cultural Vocal Bursts Intensity Prediction Fairness +1

Recall, Robustness, and Lexicographic Evaluation

no code implementations22 Feb 2023 Fernando Diaz, Bhaskar Mitra

In this light, we reflect on the measurement of recall in rankings from a formal perspective.

Fairness Retrieval

A Survey of Diversification Techniques in Search and Recommendation

no code implementations29 Dec 2022 Haolun Wu, Yansen Zhang, Chen Ma, Fuyuan Lyu, Fernando Diaz, Xue Liu

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

no code implementations11 Nov 2022 Esther Rolf, Ben Packer, Alex Beutel, Fernando Diaz

Building trustworthy, effective, and responsible machine learning systems hinges on understanding how differences in training data and modeling decisions interact to impact predictive performance.

Analyzing the Effect of Sampling in GNNs on Individual Fairness

1 code implementation8 Sep 2022 Rebecca Salganik, Fernando Diaz, Golnoosh Farnadi

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.

Fairness Recommendation Systems +1

Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural Citizenship

no code implementations2 Aug 2022 Andres Ferraro, Gustavo Ferreira, Fernando Diaz, Georgina Born

Recommender systems have become the dominant means of curating cultural content, significantly influencing the nature of individual cultural experience.

Cultural Vocal Bursts Intensity Prediction Fairness +2

On Natural Language User Profiles for Transparent and Scrutable Recommendation

no code implementations19 May 2022 Filip Radlinski, Krisztian Balog, Fernando Diaz, Lucas Dixon, Ben Wedin

Natural interaction with recommendation and personalized search systems has received tremendous attention in recent years.

Retrieval-Enhanced Machine Learning

no code implementations2 May 2022 Hamed Zamani, Fernando Diaz, Mostafa Dehghani, Donald Metzler, Michael Bendersky

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.

BIG-bench Machine Learning Information Retrieval +1

Joint Multisided Exposure Fairness for Recommendation

1 code implementation29 Apr 2022 Haolun Wu, Bhaskar Mitra, Chen Ma, Fernando Diaz, Xue Liu

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.

Exposure Fairness Information Retrieval +2

Offline Retrieval Evaluation Without Evaluation Metrics

no code implementations25 Apr 2022 Fernando Diaz, Andres Ferraro

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.

Information Retrieval Retrieval

Estimation of Fair Ranking Metrics with Incomplete Judgments

no code implementations11 Aug 2021 Ömer Kırnap, Fernando Diaz, Asia Biega, Michael Ekstrand, Ben Carterette, Emine Yilmaz

There is increasing attention to evaluating the fairness of search system ranking decisions.


The Benchmark Lottery

no code implementations14 Jul 2021 Mostafa Dehghani, Yi Tay, Alexey A. Gritsenko, Zhe Zhao, Neil Houlsby, Fernando Diaz, Donald Metzler, Oriol Vinyals

The world of empirical machine learning (ML) strongly relies on benchmarks in order to determine the relative effectiveness of different algorithms and methods.

Benchmarking BIG-bench Machine Learning +3

Fairness in Information Access Systems

no code implementations12 May 2021 Michael D. Ekstrand, Anubrata Das, Robin Burke, Fernando Diaz

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.

Fairness Information Retrieval +1

Multi-FR: A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation

no code implementations6 May 2021 Haolun Wu, Chen Ma, Bhaskar Mitra, Fernando Diaz, Xue Liu

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.

Fairness Recommendation Systems

Tip of the Tongue Known-Item Retrieval: A Case Study in Movie Identification

no code implementations18 Jan 2021 Jaime Arguello, Adam Ferguson, Emery Fine, Bhaskar Mitra, Hamed Zamani, Fernando Diaz

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.

Community Question Answering Information Retrieval +1

On the Social and Technical Challenges of Web Search Autosuggestion Moderation

no code implementations9 Jul 2020 Timothy J. Hazen, Alexandra Olteanu, Gabriella Kazai, Fernando Diaz, Michael Golebiewski

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

no code implementations30 May 2020 Hamed Zamani, Bhaskar Mitra, Everest Chen, Gord Lueck, Fernando Diaz, Paul N. Bennett, Nick Craswell, Susan T. Dumais

We also propose a model for learning representation for clarifying questions based on the user interaction data as implicit feedback.

Re-Ranking Retrieval

Operationalizing the Legal Principle of Data Minimization for Personalization

no code implementations28 May 2020 Asia J. Biega, Peter Potash, Hal Daumé III, Fernando Diaz, Michèle Finck

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')".

Recommendation Systems

Evaluating Stochastic Rankings with Expected Exposure

no code implementations27 Apr 2020 Fernando Diaz, Bhaskar Mitra, Michael D. Ekstrand, Asia J. Biega, Ben Carterette

We introduce the concept of \emph{expected exposure} as the average attention ranked items receive from users over repeated samples of the same query.

Information Retrieval Retrieval

Overview of the TREC 2019 Fair Ranking Track

no code implementations25 Mar 2020 Asia J. Biega, Fernando Diaz, Michael D. Ekstrand, Sebastian Kohlmeier

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.

Fairness Retrieval

Learning to Match Using Local and Distributed Representations of Text for Web Search

1 code implementation Proceedings of the 26th International Conference on World Wide Web, WWW '17 2017 Bhaskar Mitra, Fernando Diaz, Nick Craswell

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.

Document Ranking Information Retrieval +1

The Social Dynamics of Language Change in Online Networks

no code implementations7 Sep 2016 Rahul Goel, Sandeep Soni, Naman Goyal, John Paparrizos, Hanna Wallach, Fernando Diaz, Jacob Eisenstein

Language change is a complex social phenomenon, revealing pathways of communication and sociocultural influence.

Query Expansion with Locally-Trained Word Embeddings

no code implementations ACL 2016 Fernando Diaz, Bhaskar Mitra, Nick Craswell

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.

Ad-Hoc Information Retrieval BIG-bench Machine Learning +3

Real-Time Web Scale Event Summarization Using Sequential Decision Making

no code implementations12 May 2016 Chris Kedzie, Fernando Diaz, Kathleen McKeown

We present a system based on sequential decision making for the online summarization of massive document streams, such as those found on the web.

Decision Making

An Improved Discrete Bat Algorithm for Symmetric and Asymmetric Traveling Salesman Problems

no code implementations14 Apr 2016 Eneko Osaba, Xin-She Yang, Fernando Diaz, Pedro Lopez-Garcia, Roberto Carballedo

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

1 code implementation14 Mar 2016 David Abel, Alekh Agarwal, Fernando Diaz, Akshay Krishnamurthy, Robert E. Schapire

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.

reinforcement-learning Reinforcement Learning (RL)

Cannot find the paper you are looking for? You can Submit a new open access paper.