393 research outputs found
The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List
We are interested in supervised ranking algorithms that perform especially well near the top of the
ranked list, and are only required to perform sufficiently well on the rest of the list. In this work,
we provide a general form of convex objective that gives high-scoring examples more importance.
This “push” near the top of the list can be chosen arbitrarily large or small, based on the preference
of the user. We choose â„“p-norms to provide a specific type of push; if the user sets p larger, the
objective concentrates harder on the top of the list. We derive a generalization bound based on
the p-norm objective, working around the natural asymmetry of the problem. We then derive a
boosting-style algorithm for the problem of ranking with a push at the top. The usefulness of the
algorithm is illustrated through experiments on repository data. We prove that the minimizer of the
algorithm’s objective is unique in a specific sense. Furthermore, we illustrate how our objective is
related to quality measurements for information retrieval
An apple-to-apple comparison of Learning-to-rank algorithms in terms of Normalized Discounted Cumulative Gain
International audienceThe Normalized Discounted Cumulative Gain (NDCG) is a widely used evaluation metric for learning-to-rank (LTR) systems. NDCG is designed for ranking tasks with more than one relevance levels. There are many freely available, open source tools for computing the NDCG score for a ranked result list. Even though the definition of NDCG is unambiguous, the various tools can produce different scores for ranked lists with certain properties, deteriorating the empirical tests in many published papers and thereby making the comparison of empirical results published in different studies difficult to compare. In this study, first, we identify the major differences between the various publicly available NDCG evaluation tools. Second, based on a set of comparative experiments using a common benchmark dataset in LTR research and 6 different LTR algorithms, we demonstrate how these differences affect the overall performance of different algorithms and the final scores that are used to compare different systems
Recommender System Validation Platform
With most applications where recommender systems are used, it is impor- tant that they produce a better result than a system with no recommender, or one with a previous recommender. Deploying an untested system, even to a smaller user sample can be very costly if the system produces negative results. It is often in a developer’s interest to create several candidate systems. They need some way of comparing recommender systems before selecting one or a few to launch. While the methods of testing have been explored, and their statistical soundness motivated, in other work, it is not obvious how to do it in practice. This report describes the implementation of a modular and configurable framework, and analyses this framework with two different cases. The experimentation shows the power of how such a framework can be utilized to reduce overhead work when approaching evaluation of a new recommender system
Ranking for Relevance and Display Preferences in Complex Presentation Layouts
Learning to Rank has traditionally considered settings where given the
relevance information of objects, the desired order in which to rank the
objects is clear. However, with today's large variety of users and layouts this
is not always the case. In this paper, we consider so-called complex ranking
settings where it is not clear what should be displayed, that is, what the
relevant items are, and how they should be displayed, that is, where the most
relevant items should be placed. These ranking settings are complex as they
involve both traditional ranking and inferring the best display order. Existing
learning to rank methods cannot handle such complex ranking settings as they
assume that the display order is known beforehand. To address this gap we
introduce a novel Deep Reinforcement Learning method that is capable of
learning complex rankings, both the layout and the best ranking given the
layout, from weak reward signals. Our proposed method does so by selecting
documents and positions sequentially, hence it ranks both the documents and
positions, which is why we call it the Double-Rank Model (DRM). Our experiments
show that DRM outperforms all existing methods in complex ranking settings,
thus it leads to substantial ranking improvements in cases where the display
order is not known a priori
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