Search Results for author: Patrick John Chia

Found 10 papers, 7 papers with code

SIGIR 2021 E-Commerce Workshop Data Challenge

3 code implementations19 Apr 2021 Jacopo Tagliabue, Ciro Greco, Jean-Francis Roy, Bingqing Yu, Patrick John Chia, Federico Bianchi, Giovanni Cassani

The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for "In-session prediction for purchase intent and recommendations".

Beyond NDCG: behavioral testing of recommender systems with RecList

3 code implementations18 Nov 2021 Patrick John Chia, Jacopo Tagliabue, Federico Bianchi, Chloe He, Brian Ko

As with most Machine Learning systems, recommender systems are typically evaluated through performance metrics computed over held-out data points.

Recommendation Systems

EvalRS: a Rounded Evaluation of Recommender Systems

1 code implementation12 Jul 2022 Jacopo Tagliabue, Federico Bianchi, Tobias Schnabel, Giuseppe Attanasio, Ciro Greco, Gabriel de Souza P. Moreira, Patrick John Chia

Much of the complexity of Recommender Systems (RSs) comes from the fact that they are used as part of more complex applications and affect user experience through a varied range of user interfaces.

Recommendation Systems

EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments

1 code implementation14 Apr 2023 Federico Bianchi, Patrick John Chia, Ciro Greco, Claudio Pomo, Gabriel Moreira, Davide Eynard, Fahd Husain, Jacopo Tagliabue

EvalRS aims to bring together practitioners from industry and academia to foster a debate on rounded evaluation of recommender systems, with a focus on real-world impact across a multitude of deployment scenarios.

Fairness Informativeness +1

E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender Systems

1 code implementation20 Apr 2023 Patrick John Chia, Giuseppe Attanasio, Jacopo Tagliabue, Federico Bianchi, Ciro Greco, Gabriel de Souza P. Moreira, Davide Eynard, Fahd Husain

Recommender Systems today are still mostly evaluated in terms of accuracy, with other aspects beyond the immediate relevance of recommendations, such as diversity, long-term user retention and fairness, often taking a back seat.

Fairness Model Selection +1

How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis

1 code implementation8 Feb 2024 Federico Bianchi, Patrick John Chia, Mert Yuksekgonul, Jacopo Tagliabue, Dan Jurafsky, James Zou

We develop NegotiationArena: a flexible framework for evaluating and probing the negotiation abilities of LLM agents.

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