50 research outputs found
Sensitive and Scalable Online Evaluation with Theoretical Guarantees
Multileaved comparison methods generalize interleaved comparison methods to
provide a scalable approach for comparing ranking systems based on regular user
interactions. Such methods enable the increasingly rapid research and
development of search engines. However, existing multileaved comparison methods
that provide reliable outcomes do so by degrading the user experience during
evaluation. Conversely, current multileaved comparison methods that maintain
the user experience cannot guarantee correctness. Our contribution is two-fold.
First, we propose a theoretical framework for systematically comparing
multileaved comparison methods using the notions of considerateness, which
concerns maintaining the user experience, and fidelity, which concerns reliable
correct outcomes. Second, we introduce a novel multileaved comparison method,
Pairwise Preference Multileaving (PPM), that performs comparisons based on
document-pair preferences, and prove that it is considerate and has fidelity.
We show empirically that, compared to previous multileaved comparison methods,
PPM is more sensitive to user preferences and scalable with the number of
rankers being compared.Comment: CIKM 2017, Proceedings of the 2017 ACM on Conference on Information
and Knowledge Managemen
Unbiased Learning to Rank with Unbiased Propensity Estimation
Learning to rank with biased click data is a well-known challenge. A variety
of methods has been explored to debias click data for learning to rank such as
click models, result interleaving and, more recently, the unbiased
learning-to-rank framework based on inverse propensity weighting. Despite their
differences, most existing studies separate the estimation of click bias
(namely the \textit{propensity model}) from the learning of ranking algorithms.
To estimate click propensities, they either conduct online result
randomization, which can negatively affect the user experience, or offline
parameter estimation, which has special requirements for click data and is
optimized for objectives (e.g. click likelihood) that are not directly related
to the ranking performance of the system. In this work, we address those
problems by unifying the learning of propensity models and ranking models. We
find that the problem of estimating a propensity model from click data is a
dual problem of unbiased learning to rank. Based on this observation, we
propose a Dual Learning Algorithm (DLA) that jointly learns an unbiased ranker
and an \textit{unbiased propensity model}. DLA is an automatic unbiased
learning-to-rank framework as it directly learns unbiased ranking models from
biased click data without any preprocessing. It can adapt to the change of bias
distributions and is applicable to online learning. Our empirical experiments
with synthetic and real-world data show that the models trained with DLA
significantly outperformed the unbiased learning-to-rank algorithms based on
result randomization and the models trained with relevance signals extracted by
click models
Dynamic Collective Entity Representations for Entity Ranking
Entity ranking, i.e., successfully positioning a relevant entity at the top of the ranking for a given query, is inherently difficult due to the potential mismatch between the entity's description in a knowledge base, and the way people refer to the entity when searching for it. To counter this issue we propose a method for constructing dynamic collective entity representations. We collect entity descriptions from a variety of sources and combine them into a single entity representation by learning to weight the content from different sources that are associated with an entity for optimal retrieval effectiveness. Our method is able to add new descriptions in real time and learn the best representation as time evolves so as to capture the dynamics of how people search entities. Incorporating dynamic description sources into dynamic collective entity representations improves retrieval effectiveness by 7% over a state-of-the-art learning to rank baseline. Periodic retraining of the ranker enables higher ranking effectiveness for dynamic collective entity representations