9 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
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
Recent Developments in Recommender Systems: A Survey
In this technical survey, we comprehensively summarize the latest
advancements in the field of recommender systems. The objective of this study
is to provide an overview of the current state-of-the-art in the field and
highlight the latest trends in the development of recommender systems. The
study starts with a comprehensive summary of the main taxonomy of recommender
systems, including personalized and group recommender systems, and then delves
into the category of knowledge-based recommender systems. In addition, the
survey analyzes the robustness, data bias, and fairness issues in recommender
systems, summarizing the evaluation metrics used to assess the performance of
these systems. Finally, the study provides insights into the latest trends in
the development of recommender systems and highlights the new directions for
future research in the field
Learning from User Interactions with Rankings: A Unification of the Field
Ranking systems form the basis for online search engines and recommendation
services. They process large collections of items, for instance web pages or
e-commerce products, and present the user with a small ordered selection. The
goal of a ranking system is to help a user find the items they are looking for
with the least amount of effort. Thus the rankings they produce should place
the most relevant or preferred items at the top of the ranking. Learning to
rank is a field within machine learning that covers methods which optimize
ranking systems w.r.t. this goal. Traditional supervised learning to rank
methods utilize expert-judgements to evaluate and learn, however, in many
situations such judgements are impossible or infeasible to obtain. As a
solution, methods have been introduced that perform learning to rank based on
user clicks instead. The difficulty with clicks is that they are not only
affected by user preferences, but also by what rankings were displayed.
Therefore, these methods have to prevent being biased by other factors than
user preference. This thesis concerns learning to rank methods based on user
clicks and specifically aims to unify the different families of these methods.
As a whole, the second part of this thesis proposes a framework that bridges
many gaps between areas of online, counterfactual, and supervised learning to
rank. It has taken approaches, previously considered independent, and unified
them into a single methodology for widely applicable and effective learning to
rank from user clicks.Comment: PhD Thesis of Harrie Oosterhuis defended at the University of
Amsterdam on November 27th 202