DOCTORAL ABSTRACT

Scalability and Performance of Random Forest
based Learning-to-Rank for Information
RetrievalMuhammad Ibrahim
Faculty of Information Technology, Monash University, Australia
313ibrahim@gmail.com

Abstract
For a query submitted by a user, the goal of an information retrieval system is to returna list of documents which are highly relevant with respect to that query. Traditionallydifferent scoring methods, ranging from simple heuristic models to probabilistic models,
have been used for this task. Recently researchers have started to use supervised machinelearning techniques for solving this problem which is then called the learning-to-rank (LtR)
problem. Many supervised learning methods have been tested so far with empirical successover conventional methods [4].
The random forest is a relatively simple but effective and efficient learning algorithm
which aggregates the predictions of a large number of independent and variant base learners, namely decision trees. Its major benefits over other state-of-the-art methods include
inherent parallelizability, ease of tuning and competitive performance. These benefits attract researchers across various disciplines where a random forest is a very popular choice.
However, for LtR task, the random forest has not been thoroughly investigated.
In this research, we investigate the random forest based LtR algorithms. We aim at
improving the efficiency, effectiveness, and understanding of these algorithms. With respect
to the first goal, we employ undersampling techniques and leverage the inherent structureof a random forest to achieve better scalability, especially for highly imbalanced datasets[2]. We also reduce the correlation among the trees to reduce learning time and to improve
performance [3]. With respect to the second goal, we investigate various objective functions
ranging from completely randomized splitting criterion to so-called listwise splitting [1]. We
also conduct a thorough study on random forest based pointwise algorithms. With respect tothe third goal, we develop methods for estimating the bias and variance of rank-learning algorithms, and examine their empirical behavior against parameters of the learning algorithm.
The thesis is available at: https://figshare.com/articles/Scalability_and_Performance_of_Random_Forest_based_Learning-to-Rank_for_Information_Retrieval/4407443

ACM SIGIR Forum73

Vol. 51 No. 1 June 2017

References[1] Muhammad Ibrahim and Mark Carman. Comparing pointwise and listwise objective functions for random forest based learning-to-rank. ACM Transactions on Information Systems (TOIS), 34(4): Article No.: 20, 2016.
[2] Muhammad Ibrahim and Mark Carman. Undersampling techniques to re-balance training
data for large scale learning-to-rank. Information Retrieval Technology, pages 444-457.
Springer, 2014.
[3] Muhammad Ibrahim and Mark Carman. Improving scalability and performance of random
forest based learning-to-rank algorithms by aggressive subsampling. Proceedings of the12th Australasian Data Mining Conference, pages 91-99. 2014.
[4] Muhammad Ibrahim and Manzur Murshed. From tf-idf to learning-to-rank: An overview.
Handbook of Research on Innovations in Information Retrieval, Analysis, and Management, pages 62-109. IGI Global, USA, 2016.

ACM SIGIR Forum74

Vol. 51 No. 1 June 2017