Online Structured Prediction via Coactive Learning

05/18/2012
by   Pannaga Shivaswamy, et al.
0

We propose Coactive Learning as a model of interaction between a learning system and a human user, where both have the common goal of providing results of maximum utility to the user. At each step, the system (e.g. search engine) receives a context (e.g. query) and predicts an object (e.g. ranking). The user responds by correcting the system if necessary, providing a slightly improved -- but not necessarily optimal -- object as feedback. We argue that such feedback can often be inferred from observable user behavior, for example, from clicks in web-search. Evaluating predictions by their cardinal utility to the user, we propose efficient learning algorithms that have O(1/√(T)) average regret, even though the learning algorithm never observes cardinal utility values as in conventional online learning. We demonstrate the applicability of our model and learning algorithms on a movie recommendation task, as well as ranking for web-search.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/03/2011

Online Learning with Preference Feedback

We propose a new online learning model for learning with preference feed...
research
06/06/2018

TopRank: A practical algorithm for online stochastic ranking

Online learning to rank is a sequential decision-making problem where in...
research
02/03/2022

Learning from a Learning User for Optimal Recommendations

In real-world recommendation problems, especially those with a formidabl...
research
11/03/2017

Learning to Bid Without Knowing your Value

We address online learning in complex auction settings, such as sponsore...
research
10/12/2021

Optimizing Ranking Systems Online as Bandits

Ranking system is the core part of modern retrieval and recommender syst...
research
01/25/2023

Overcoming Prior Misspecification in Online Learning to Rank

The recent literature on online learning to rank (LTR) has established t...
research
08/15/2023

Delphic Costs and Benefits in Web Search: A utilitarian and historical analysis

We present a new framework to conceptualize and operationalize the total...

Please sign up or login with your details

Forgot password? Click here to reset