Search Results for author: Gaurush Hiranandani

Found 12 papers, 2 papers with code

Optimization and Analysis of the pAp@k Metric for Recommender Systems

no code implementations ICML 2020 Gaurush Hiranandani, Warut Vijitbenjaronk, Sanmi Koyejo, Prateek Jain

Modern recommendation and notification systems must be robust to data imbalance, limitations on the number of recommendations/notifications, and heterogeneous engagement profiles across users.

Recommendation Systems

Metric Elicitation; Moving from Theory to Practice

no code implementations7 Dec 2022 Safinah Ali, Sohini Upadhyay, Gaurush Hiranandani, Elena L. Glassman, Oluwasanmi Koyejo

Specifically, we create a web-based ME interface and conduct a user study that elicits users' preferred metrics in a binary classification setting.


Classification Performance Metric Elicitation and its Applications

no code implementations19 Aug 2022 Gaurush Hiranandani

Specifically, we provide novel strategies for eliciting linear and linear-fractional metrics for binary and multiclass classification problems, which are then extended to a framework that elicits group-fair performance metrics in the presence of multiple sensitive groups.


Quadratic Metric Elicitation for Fairness and Beyond

1 code implementation3 Nov 2020 Gaurush Hiranandani, Jatin Mathur, Harikrishna Narasimhan, Oluwasanmi Koyejo

Metric elicitation is a recent framework for eliciting classification performance metrics that best reflect implicit user preferences based on the task and context.


Rich-Item Recommendations for Rich-Users: Exploiting Dynamic and Static Side Information

no code implementations28 Jan 2020 Amar Budhiraja, Gaurush Hiranandani, Darshak Chhatbar, Aditya Sinha, Navya Yarrabelly, Ayush Choure, Oluwasanmi Koyejo, Prateek Jain

In this paper, we study the problem of recommendation system where the users and items to be recommended are rich data structures with multiple entity types and with multiple sources of side-information in the form of graphs.


Multiclass Performance Metric Elicitation

no code implementations NeurIPS 2019 Gaurush Hiranandani, Shant Boodaghians, Ruta Mehta, Oluwasanmi O. Koyejo

Metric Elicitation is a principled framework for selecting the performance metric that best reflects implicit user preferences.

Classification General Classification

Online Diverse Learning to Rank from Partial-Click Feedback

no code implementations1 Nov 2018 Prakhar Gupta, Gaurush Hiranandani, Harvineet Singh, Branislav Kveton, Zheng Wen, Iftikhar Ahamath Burhanuddin

We assume that the user examines the list of recommended items until the user is attracted by an item, which is clicked, and does not examine the rest of the items.

Learning-To-Rank Recommendation Systems

Clustered Monotone Transforms for Rating Factorization

no code implementations31 Oct 2018 Gaurush Hiranandani, Raghav Somani, Oluwasanmi Koyejo, Sreangsu Acharyya

This non-linear transformation of the rating scale shatters the low-rank structure of the rating matrix, therefore resulting in a poor fit and consequentially, poor recommendations.

Recommendation Systems regression

Generating Appealing Brand Names

no code implementations28 Jun 2017 Gaurush Hiranandani, Pranav Maneriker, Harsh Jhamtani

Providing appealing brand names to newly launched products, newly formed companies or for renaming existing companies is highly important as it can play a crucial role in deciding its success or failure.

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