Listwise Learning to Rank with Deep Q-Networks

02/13/2020
by   Abhishek Sharma, et al.
0

Learning to Rank is the problem involved with ranking a sequence of documents based on their relevance to a given query. Deep Q-Learning has been shown to be a useful method for training an agent in sequential decision making. In this paper, we show that DeepQRank, our deep q-learning to rank agent, demonstrates performance that can be considered state-of-the-art. Though less computationally efficient than a supervised learning approach such as linear regression, our agent has fewer limitations in terms of which format of data it can use for training and evaluation. We run our algorithm against Microsoft's LETOR listwise dataset and achieve an NDCG@1 (ranking accuracy in the range [0,1]) of 0.5075, narrowly beating out the leading supervised learning model, SVMRank (0.4958).

READ FULL TEXT
research
09/15/2019

MarlRank: Multi-agent Reinforced Learning to Rank

When estimating the relevancy between a query and a document, ranking mo...
research
11/29/2015

MidRank: Learning to rank based on subsequences

We present a supervised learning to rank algorithm that effectively orde...
research
12/14/2018

Scaling shared model governance via model splitting

Currently the only techniques for sharing governance of a deep learning ...
research
08/31/2018

A Supervised Learning Approach For Heading Detection

As the Portable Document Format (PDF) file format increases in popularit...
research
01/21/2021

Assessing the Benefits of Model Ensembles in Neural Re-Ranking for Passage Retrieval

Our work aimed at experimentally assessing the benefits of model ensembl...
research
08/26/2022

Non-probabilistic Supervised Learning for Non-linear Convex Variational Problems

In this article we propose, based on a non-probabilistic supervised lear...
research
08/14/2016

Stacked Approximated Regression Machine: A Simple Deep Learning Approach

With the agreement of my coauthors, I Zhangyang Wang would like to withd...

Please sign up or login with your details

Forgot password? Click here to reset