Search Results for author:
Found 7 papers, 1 papers with code
Improving Sequential Query Recommendation with Immediate User Feedback
We propose to augment the transformer-based causal language models for query recommendations to adapt to the immediate user feedback using multi-armed bandit (MAB) framework.
Re-ranking Based Diversification: A Unifying View
We analyze different re-ranking algorithms for diversification and show that majority of them are based on maximizing submodular/modular functions from the class of parameterized concave/linear over modular functions.
Risk Aware Ranking for Top-$k$ Recommendations
Given an incomplete ratings data over a set of users and items, the preference completion problem aims to estimate a personalized total preference order over a subset of the items.
Reuse and Adaptation for Entity Resolution through Transfer Learning
Entity resolution (ER) is one of the fundamental problems in data integration, where machine learning (ML) based classifiers often provide the state-of-the-art results.
SAGA: A Submodular Greedy Algorithm For Group Recommendation
In this paper, we propose a unified framework and an algorithm for the problem of group recommendation where a fixed number of items or alternatives can be recommended to a group of users.
Theory of Optimizing Pseudolinear Performance Measures: Application to F-measure
We study the theoretical properties of a subset of non-linear performance measures called pseudo-linear performance measures which includes $F$-measure, \emph{Jaccard Index}, among many others.
Topic Extraction and Bundling of Related Scientific Articles
In this report we propose an automatic two-step approach for topic extraction and bundling of related articles from a set of scientific articles in real-time.
