36,554 research outputs found
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
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
NAIS: Neural Attentive Item Similarity Model for Recommendation
Item-to-item collaborative filtering (aka. item-based CF) has been long used
for building recommender systems in industrial settings, owing to its
interpretability and efficiency in real-time personalization. It builds a
user's profile as her historically interacted items, recommending new items
that are similar to the user's profile. As such, the key to an item-based CF
method is in the estimation of item similarities. Early approaches use
statistical measures such as cosine similarity and Pearson coefficient to
estimate item similarities, which are less accurate since they lack tailored
optimization for the recommendation task. In recent years, several works
attempt to learn item similarities from data, by expressing the similarity as
an underlying model and estimating model parameters by optimizing a
recommendation-aware objective function. While extensive efforts have been made
to use shallow linear models for learning item similarities, there has been
relatively less work exploring nonlinear neural network models for item-based
CF.
In this work, we propose a neural network model named Neural Attentive Item
Similarity model (NAIS) for item-based CF. The key to our design of NAIS is an
attention network, which is capable of distinguishing which historical items in
a user profile are more important for a prediction. Compared to the
state-of-the-art item-based CF method Factored Item Similarity Model (FISM),
our NAIS has stronger representation power with only a few additional
parameters brought by the attention network. Extensive experiments on two
public benchmarks demonstrate the effectiveness of NAIS. This work is the first
attempt that designs neural network models for item-based CF, opening up new
research possibilities for future developments of neural recommender systems
Controlling Fairness and Bias in Dynamic Learning-to-Rank
Rankings are the primary interface through which many online platforms match
users to items (e.g. news, products, music, video). In these two-sided markets,
not only the users draw utility from the rankings, but the rankings also
determine the utility (e.g. exposure, revenue) for the item providers (e.g.
publishers, sellers, artists, studios). It has already been noted that
myopically optimizing utility to the users, as done by virtually all
learning-to-rank algorithms, can be unfair to the item providers. We,
therefore, present a learning-to-rank approach for explicitly enforcing
merit-based fairness guarantees to groups of items (e.g. articles by the same
publisher, tracks by the same artist). In particular, we propose a learning
algorithm that ensures notions of amortized group fairness, while
simultaneously learning the ranking function from implicit feedback data. The
algorithm takes the form of a controller that integrates unbiased estimators
for both fairness and utility, dynamically adapting both as more data becomes
available. In addition to its rigorous theoretical foundation and convergence
guarantees, we find empirically that the algorithm is highly practical and
robust.Comment: First two authors contributed equally. In Proceedings of the 43rd
International ACM SIGIR Conference on Research and Development in Information
Retrieval 202
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