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These posts describe papers published by the PIReT crew.

Papers

Retrieving and Recommending for the Classroom

Michael D. Ekstrand, Ion Madrazo Azpiazu, Katherine Landau Wright, and Maria Soledad Pera. 2018. Retrieving and Recommending for the Classroom: Stakeholders, Objectives, Resources, and Users. In Proceedings of the ComplexRec 2018 Second Workshop on Recommendation in Complex Scenarios at RecSys … (Read More) Retrieving and Recommending for the Classroom

From Recommendation to Curation

Nevena Dragovic, Ion Madrazo Azpiazu, and Maria Soledad Pera. 2018. From Recommendation to Curation: When the system becomes your personal docent. In Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS); co-located with ACM … (Read More) From Recommendation to Curation

Monte Carlo Estimates of Evaluation Metric Error and Bias

Mucun Tian and Michael D. Ekstrand. 2018. Monte Carlo Estimates of Evaluation Metric Error and Bias. At The REVEAL 2018 Workshop on Offline Evaluation for Recommender Systems. Abstract Traditional offline evaluations of recommender systems apply metrics from machine learning and … (Read More) Monte Carlo Estimates of Evaluation Metric Error and Bias

The LKPY Package for Recommender Systems Experiments

Michael D. Ekstrand. 2018. The LKPY Package for Recommender Systems Experiments: Next-Generation Tools and Lessons Learned from the LensKit Project. At The REVEAL 2018 Workshop on Offline Evaluation for Recommender Systems. Abstract Since 2010, we have built and maintained LensKit, … (Read More) The LKPY Package for Recommender Systems Experiments

Who is Really Affected by Fraudulent Reviews?

Anu Shresta, Francesca Spezzano, and Maria Soledad Pera. 2018. Who is Really Affected by Fraudulent Reviews?: An analysis of shilling attacks on recommender systems in real-world scenarios. In Poster Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18). (Read More) Who is Really Affected by Fraudulent Reviews?

Can we leverage rating patterns from traditional users to enhance recommendations for children?

Ion Madrazo Azpiazu, Micahel Green, Oghenemaro Anuyah, and Maria Soledad Pera. 2018. Can we leverage rating patterns from traditional users to enhance recommendations for children? In Poster Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18). (Read More) Can we leverage rating patterns from traditional users to enhance recommendations for children?

Recommending Social-Interactive Games for Adults with Autism Spectrum Disorders (ASD)

Yiu-Kai Ng and Maria Soledad Pera. 2018. Recommending Social-Interactive Games for Adults with Autism Spectrum Disorders (ASD). In Proceedings of the 12th ACM Conference on Recommender Systems (ACM RecSys). (Read More) Recommending Social-Interactive Games for Adults with Autism Spectrum Disorders (ASD)

Exploring Author Gender in Book Rating and Recommendation

Michael D. Ekstrand, Mucun Tian, Mohammed R. Imran Kazi, Hoda Mehrpouyan, and Daniel Kluver. 2018. Exploring Author Gender in Book Rating and Recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18). DOI:10.1145/3240323.3240373. Abstract Collaborative filtering algorithms … (Read More) Exploring Author Gender in Book Rating and Recommendation

Recommending Texts to Children with an Expert in the Loop

Maria Soledad Pera, Katherine Wright, Michael D. Ekstrand. 2018. Recommending Texts to Children with an Expert in the Loop. In Proceedings of the 2nd International Workshop on Children & Recommender Systems (KidRec) at IDC 2018. DOI:10.18122/cs_facpubs/140/boisestate. ABSTRACT In this position … (Read More) Recommending Texts to Children with an Expert in the Loop

Privacy for All

Michael D. Ekstrand, Rezvan Joshaghani, and Hoda Mehrpouyan. 2018. Privacy for All: Ensuring Fair and Equitable Privacy Protections. In Proceedings of the Conference on Fairness, Accountability and Transparency. Abstract In this position paper, we argue for applying recent research on … (Read More) Privacy for All