3,700 research outputs found
Estimating Position Bias without Intrusive Interventions
Presentation bias is one of the key challenges when learning from implicit
feedback in search engines, as it confounds the relevance signal. While it was
recently shown how counterfactual learning-to-rank (LTR) approaches
\cite{Joachims/etal/17a} can provably overcome presentation bias when
observation propensities are known, it remains to show how to effectively
estimate these propensities. In this paper, we propose the first method for
producing consistent propensity estimates without manual relevance judgments,
disruptive interventions, or restrictive relevance modeling assumptions. First,
we show how to harvest a specific type of intervention data from historic
feedback logs of multiple different ranking functions, and show that this data
is sufficient for consistent propensity estimation in the position-based model.
Second, we propose a new extremum estimator that makes effective use of this
data. In an empirical evaluation, we find that the new estimator provides
superior propensity estimates in two real-world systems -- Arxiv Full-text
Search and Google Drive Search. Beyond these two points, we find that the
method is robust to a wide range of settings in simulation studies
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
To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions
Learning to Rank (LTR) from user interactions is challenging as user feedback
often contains high levels of bias and noise. At the moment, two methodologies
for dealing with bias prevail in the field of LTR: counterfactual methods that
learn from historical data and model user behavior to deal with biases; and
online methods that perform interventions to deal with bias but use no explicit
user models. For practitioners the decision between either methodology is very
important because of its direct impact on end users. Nevertheless, there has
never been a direct comparison between these two approaches to unbiased LTR. In
this study we provide the first benchmarking of both counterfactual and online
LTR methods under different experimental conditions. Our results show that the
choice between the methodologies is consequential and depends on the presence
of selection bias, and the degree of position bias and interaction noise. In
settings with little bias or noise counterfactual methods can obtain the
highest ranking performance; however, in other circumstances their optimization
can be detrimental to the user experience. Conversely, online methods are very
robust to bias and noise but require control over the displayed rankings. Our
findings confirm and contradict existing expectations on the impact of
model-based and intervention-based methods in LTR, and allow practitioners to
make an informed decision between the two methodologies.Comment: SIGIR 201
Double Clipping: Less-Biased Variance Reduction in Off-Policy Evaluation
"Clipping" (a.k.a. importance weight truncation) is a widely used
variance-reduction technique for counterfactual off-policy estimators. Like
other variance-reduction techniques, clipping reduces variance at the cost of
increased bias. However, unlike other techniques, the bias introduced by
clipping is always a downward bias (assuming non-negative rewards), yielding a
lower bound on the true expected reward. In this work we propose a simple
extension, called , which aims to compensate this
downward bias and thus reduce the overall bias, while maintaining the variance
reduction properties of the original estimator.Comment: Presented at CONSEQUENCES '23 workshop at RecSys 2023 conference in
Singapor
Re-examining assumptions in fair and unbiased learning to rank
In this thesis, we re-examine the assumptions of existing methods for bias correction and fairness optimization in ranking. Consequently, we propose methods that are more general than the existing ones, in the sense that they rely on less assumptions, or they are applicable in more situations. On the bias side, we first show that the click model assumption matters and propose cascade model-based inverse propensity scoring (IPS). Next, we prove that the unbiasedness of IPS relies on the assumption that the clicks do not suffer from trust bias. When trust bias exists, we extend IPS and propose the affine correction (AC) method and prove that, in contrast to IPS, it gives unbiased estimates of the relevance. Finally, we show that the unbiasedness proofs of IPS and AC are conditioned on an accurate estimation of the bias parameters, and propose a bias correction method that does not rely on relevance estimation. On the fairness side, we re-examine the implicit assumption that fair distribution of exposure leads to fair treatment by the users. We argue that fairness of exposure is necessary but not enough for a fair treatment and propose a correction method for this type of bias. Finally, we notice that the existing general post-processing framework for optimizing fairness of ranking metrics is based on the Plackett-Luce distribution, the optimization of which has room for improvement for queries with a small number of repeating sessions. To close this gap, we propose a new permutation distribution based on permutation graphs
Unconfounded Propensity Estimation for Unbiased Ranking
The goal of unbiased learning to rank (ULTR) is to leverage implicit user
feedback for optimizing learning-to-rank systems. Among existing solutions,
automatic ULTR algorithms that jointly learn user bias models (i.e., propensity
models) with unbiased rankers have received a lot of attention due to their
superior performance and low deployment cost in practice. Despite their
theoretical soundness, the effectiveness is usually justified under a weak
logging policy, where the ranking model can barely rank documents according to
their relevance to the query. However, when the logging policy is strong, e.g.,
an industry-deployed ranking policy, the reported effectiveness cannot be
reproduced. In this paper, we first investigate ULTR from a causal perspective
and uncover a negative result: existing ULTR algorithms fail to address the
issue of propensity overestimation caused by the query-document relevance
confounder. Then, we propose a new learning objective based on backdoor
adjustment and highlight its differences from conventional propensity models,
which reveal the prevalence of propensity overestimation. On top of that, we
introduce a novel propensity model called Logging-Policy-aware Propensity (LPP)
model and its distinctive two-step optimization strategy, which allows for the
joint learning of LPP and ranking models within the automatic ULTR framework,
and actualize the unconfounded propensity estimation for ULTR. Extensive
experiments on two benchmarks demonstrate the effectiveness and
generalizability of the proposed method.Comment: 11 pages, 5 figure
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