Projects from That Recommender Systems Lab.

Description

In this project, we are exploring the characteristics of recommendation results that have been reranked for fairness concerns and their differences under pipelined versus joint optimization. Pipelined optimization is one of the most common approaches and leaves the recommender system t...

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Description

In this project, we have proposed a model to formalize multistakeholder fairness in recommender systems as a two stage social choice problem. We express recommendation fairness as a novel combination of an allocation and an aggregation problem, which integrate both fairness concerns an...

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Tutorial

Experimentation with fairness-aware recommendation using librec-auto

The field of machine learning fairness has developed some well-understood metrics, methodologies, and data sets for experimenting with and developing classification algorithms with an eye to their fairness properties....

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Description

Recommender systems are pervasive in e-commerce and widely used in other applications, providing personalized suggestions to help users find items of interest in large online catalogs. As they have become more prevalent, recommender systems have moved from areas of consumer taste, such...

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Description

In this project, we are developing a Python wrapper for the LibRec recommendation systems platform. (Learn more at librec.net) The wrapper makes it easier for researchers to experiment with a variety of algorithms and configuration options.

The tool was presented as a demo at the...

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