30 research outputs found
Reusing historical interaction data for faster online learning to rank for IR
Online learning to rank for information retrieval (IR) holds promise for allowing the development of "self-learning" search engines that can automatically adjust to their users. With the large amount of e.g., click data that can be collected in web search settings, such techniques could enable highly scalable ranking optimization. However, feedback obtained from user interactions is noisy, and developing approaches that can learn from this feedback quickly and reliably is a major challenge. In this paper we investigate whether and how previously collected (historical) interaction data can be used to speed up learning in online learning to rank for IR. We devise the first two methods that can utilize historical data (1) to make feedback available during learning more reliable and (2) to preselect candidate ranking functions to be evaluated in interactions with users of the retrieval system. We evaluate both approaches on 9 learning to rank data sets and find that historical data can speed up learning, leading to substantially and significantly higher online performance. In particular, our pre-selection method proves highly effective at compensating for noise in user feedback. Our results show that historical data can be used to make online learning to rank for IR much more effective than previously possible, especially when feedback is noisy
Optimizing Ranking Models in an Online Setting
Online Learning to Rank (OLTR) methods optimize ranking models by directly
interacting with users, which allows them to be very efficient and responsive.
All OLTR methods introduced during the past decade have extended on the
original OLTR method: Dueling Bandit Gradient Descent (DBGD). Recently, a
fundamentally different approach was introduced with the Pairwise
Differentiable Gradient Descent (PDGD) algorithm. To date the only comparisons
of the two approaches are limited to simulations with cascading click models
and low levels of noise. The main outcome so far is that PDGD converges at
higher levels of performance and learns considerably faster than DBGD-based
methods. However, the PDGD algorithm assumes cascading user behavior,
potentially giving it an unfair advantage. Furthermore, the robustness of both
methods to high levels of noise has not been investigated. Therefore, it is
unclear whether the reported advantages of PDGD over DBGD generalize to
different experimental conditions. In this paper, we investigate whether the
previous conclusions about the PDGD and DBGD comparison generalize from ideal
to worst-case circumstances. We do so in two ways. First, we compare the
theoretical properties of PDGD and DBGD, by taking a critical look at
previously proven properties in the context of ranking. Second, we estimate an
upper and lower bound on the performance of methods by simulating both ideal
user behavior and extremely difficult behavior, i.e., almost-random
non-cascading user models. Our findings show that the theoretical bounds of
DBGD do not apply to any common ranking model and, furthermore, that the
performance of DBGD is substantially worse than PDGD in both ideal and
worst-case circumstances. These results reproduce previously published findings
about the relative performance of PDGD vs. DBGD and generalize them to
extremely noisy and non-cascading circumstances.Comment: European Conference on Information Retrieval (ECIR) 201
Unbiased Learning to Rank: Counterfactual and Online Approaches
This tutorial covers and contrasts the two main methodologies in unbiased
Learning to Rank (LTR): Counterfactual LTR and Online LTR. There has long been
an interest in LTR from user interactions, however, this form of implicit
feedback is very biased. In recent years, unbiased LTR methods have been
introduced to remove the effect of different types of bias caused by
user-behavior in search. For instance, a well addressed type of bias is
position bias: the rank at which a document is displayed heavily affects the
interactions it receives. Counterfactual LTR methods deal with such types of
bias by learning from historical interactions while correcting for the effect
of the explicitly modelled biases. Online LTR does not use an explicit user
model, in contrast, it learns through an interactive process where randomized
results are displayed to the user. Through randomization the effect of
different types of bias can be removed from the learning process. Though both
methodologies lead to unbiased LTR, their approaches differ considerably,
furthermore, so do their theoretical guarantees, empirical results, effects on
the user experience during learning, and applicability. Consequently, for
practitioners the choice between the two is very substantial. By providing an
overview of both approaches and contrasting them, we aim to provide an
essential guide to unbiased LTR so as to aid in understanding and choosing
between methodologies.Comment: Abstract for tutorial appearing at SIGIR 201
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback
We develop a learning principle and an efficient algorithm for batch learning
from logged bandit feedback. This learning setting is ubiquitous in online
systems (e.g., ad placement, web search, recommendation), where an algorithm
makes a prediction (e.g., ad ranking) for a given input (e.g., query) and
observes bandit feedback (e.g., user clicks on presented ads). We first address
the counterfactual nature of the learning problem through propensity scoring.
Next, we prove generalization error bounds that account for the variance of the
propensity-weighted empirical risk estimator. These constructive bounds give
rise to the Counterfactual Risk Minimization (CRM) principle. We show how CRM
can be used to derive a new learning method -- called Policy Optimizer for
Exponential Models (POEM) -- for learning stochastic linear rules for
structured output prediction. We present a decomposition of the POEM objective
that enables efficient stochastic gradient optimization. POEM is evaluated on
several multi-label classification problems showing substantially improved
robustness and generalization performance compared to the state-of-the-art.Comment: 10 page
Effective Evaluation using Logged Bandit Feedback from Multiple Loggers
Accurately evaluating new policies (e.g. ad-placement models, ranking
functions, recommendation functions) is one of the key prerequisites for
improving interactive systems. While the conventional approach to evaluation
relies on online A/B tests, recent work has shown that counterfactual
estimators can provide an inexpensive and fast alternative, since they can be
applied offline using log data that was collected from a different policy
fielded in the past. In this paper, we address the question of how to estimate
the performance of a new target policy when we have log data from multiple
historic policies. This question is of great relevance in practice, since
policies get updated frequently in most online systems. We show that naively
combining data from multiple logging policies can be highly suboptimal. In
particular, we find that the standard Inverse Propensity Score (IPS) estimator
suffers especially when logging and target policies diverge -- to a point where
throwing away data improves the variance of the estimator. We therefore propose
two alternative estimators which we characterize theoretically and compare
experimentally. We find that the new estimators can provide substantially
improved estimation accuracy.Comment: KDD 201
Simulating Users in Interactive Web Table Retrieval
Considering the multimodal signals of search items is beneficial for
retrieval effectiveness. Especially in web table retrieval (WTR) experiments,
accounting for multimodal properties of tables boosts effectiveness. However,
it still remains an open question how the single modalities affect user
experience in particular. Previous work analyzed WTR performance in ad-hoc
retrieval benchmarks, which neglects interactive search behavior and limits the
conclusion about the implications for real-world user environments.
To this end, this work presents an in-depth evaluation of simulated
interactive WTR search sessions as a more cost-efficient and reproducible
alternative to real user studies. As a first of its kind, we introduce
interactive query reformulation strategies based on Doc2Query, incorporating
cognitive states of simulated user knowledge. Our evaluations include two
perspectives on user effectiveness by considering different cost paradigms,
namely query-wise and time-oriented measures of effort. Our multi-perspective
evaluation scheme reveals new insights about query strategies, the impact of
modalities, and different user types in simulated WTR search sessions.Comment: 4 pages + references; accepted at CIKM'2
Differentiable Unbiased Online Learning to Rank
Online Learning to Rank (OLTR) methods optimize rankers based on user
interactions. State-of-the-art OLTR methods are built specifically for linear
models. Their approaches do not extend well to non-linear models such as neural
networks. We introduce an entirely novel approach to OLTR that constructs a
weighted differentiable pairwise loss after each interaction: Pairwise
Differentiable Gradient Descent (PDGD). PDGD breaks away from the traditional
approach that relies on interleaving or multileaving and extensive sampling of
models to estimate gradients. Instead, its gradient is based on inferring
preferences between document pairs from user clicks and can optimize any
differentiable model. We prove that the gradient of PDGD is unbiased w.r.t.
user document pair preferences. Our experiments on the largest publicly
available Learning to Rank (LTR) datasets show considerable and significant
improvements under all levels of interaction noise. PDGD outperforms existing
OLTR methods both in terms of learning speed as well as final convergence.
Furthermore, unlike previous OLTR methods, PDGD also allows for non-linear
models to be optimized effectively. Our results show that using a neural
network leads to even better performance at convergence than a linear model. In
summary, PDGD is an efficient and unbiased OLTR approach that provides a better
user experience than previously possible.Comment: Conference on Information and Knowledge Management 201
Policy-Aware Unbiased Learning to Rank for Top-k Rankings
Counterfactual Learning to Rank (LTR) methods optimize ranking systems using
logged user interactions that contain interaction biases. Existing methods are
only unbiased if users are presented with all relevant items in every ranking.
There is currently no existing counterfactual unbiased LTR method for top-k
rankings. We introduce a novel policy-aware counterfactual estimator for LTR
metrics that can account for the effect of a stochastic logging policy. We
prove that the policy-aware estimator is unbiased if every relevant item has a
non-zero probability to appear in the top-k ranking. Our experimental results
show that the performance of our estimator is not affected by the size of k:
for any k, the policy-aware estimator reaches the same retrieval performance
while learning from top-k feedback as when learning from feedback on the full
ranking. Lastly, we introduce novel extensions of traditional LTR methods to
perform counterfactual LTR and to optimize top-k metrics. Together, our
contributions introduce the first policy-aware unbiased LTR approach that
learns from top-k feedback and optimizes top-k metrics. As a result,
counterfactual LTR is now applicable to the very prevalent top-k ranking
setting in search and recommendation.Comment: SIGIR 2020 full conference pape