184 research outputs found
Pairwise meta-rules for better meta-learning-based algorithm ranking
In this paper, we present a novel meta-feature generation method in the context of meta-learning, which is based on rules that compare the performance of individual base learners in a one-against-one manner. In addition to these new meta-features, we also introduce a new meta-learner called Approximate Ranking Tree Forests (ART Forests) that performs very competitively when compared with several state-of-the-art meta-learners. Our experimental results are based on a large collection of datasets and show that the proposed new techniques can improve the overall performance of meta-learning for algorithm ranking significantly. A key point in our approach is that each performance figure of any base learner for any specific dataset is generated by optimising the parameters of the base learner separately for each dataset
Learning from User Interactions with Rankings: A Unification of the Field
Ranking systems form the basis for online search engines and recommendation
services. They process large collections of items, for instance web pages or
e-commerce products, and present the user with a small ordered selection. The
goal of a ranking system is to help a user find the items they are looking for
with the least amount of effort. Thus the rankings they produce should place
the most relevant or preferred items at the top of the ranking. Learning to
rank is a field within machine learning that covers methods which optimize
ranking systems w.r.t. this goal. Traditional supervised learning to rank
methods utilize expert-judgements to evaluate and learn, however, in many
situations such judgements are impossible or infeasible to obtain. As a
solution, methods have been introduced that perform learning to rank based on
user clicks instead. The difficulty with clicks is that they are not only
affected by user preferences, but also by what rankings were displayed.
Therefore, these methods have to prevent being biased by other factors than
user preference. This thesis concerns learning to rank methods based on user
clicks and specifically aims to unify the different families of these methods.
As a whole, the second part of this thesis proposes a framework that bridges
many gaps between areas of online, counterfactual, and supervised learning to
rank. It has taken approaches, previously considered independent, and unified
them into a single methodology for widely applicable and effective learning to
rank from user clicks.Comment: PhD Thesis of Harrie Oosterhuis defended at the University of
Amsterdam on November 27th 202
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