Perceptron-like Algorithms and Generalization Bounds for Learning to Rank

05/03/2014
by   Sougata Chaudhuri, et al.
0

Learning to rank is a supervised learning problem where the output space is the space of rankings but the supervision space is the space of relevance scores. We make theoretical contributions to the learning to rank problem both in the online and batch settings. First, we propose a perceptron-like algorithm for learning a ranking function in an online setting. Our algorithm is an extension of the classic perceptron algorithm for the classification problem. Second, in the setting of batch learning, we introduce a sufficient condition for convex ranking surrogates to ensure a generalization bound that is independent of number of objects per query. Our bound holds when linear ranking functions are used: a common practice in many learning to rank algorithms. En route to developing the online algorithm and generalization bound, we propose a novel family of listwise large margin ranking surrogates. Our novel surrogate family is obtained by modifying a well-known pairwise large margin ranking surrogate and is distinct from the listwise large margin surrogates developed using the structured prediction framework. Using the proposed family, we provide a guaranteed upper bound on the cumulative NDCG (or MAP) induced loss under the perceptron-like algorithm. We also show that the novel surrogates satisfy the generalization bound condition.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/04/2015

Perceptron like Algorithms for Online Learning to Rank

Perceptron is a classic online algorithm for learning a classification f...
research
03/02/2017

A Generic Online Parallel Learning Framework for Large Margin Models

To speed up the training process, many existing systems use parallel tec...
research
11/25/2019

Cumulative Sum Ranking

The goal of Ordinal Regression is to find a rule that ranks items from a...
research
12/24/2019

Online Algorithms for Multiclass Classification using Partial Labels

In this paper, we propose online algorithms for multiclass classificatio...
research
02/12/2018

PRIL: Perceptron Ranking Using Interval Labeled Data

In this paper, we propose an online learning algorithm PRIL for learning...
research
05/26/2015

Surrogate Functions for Maximizing Precision at the Top

The problem of maximizing precision at the top of a ranked list, often d...
research
11/29/2015

MidRank: Learning to rank based on subsequences

We present a supervised learning to rank algorithm that effectively orde...

Please sign up or login with your details

Forgot password? Click here to reset