User simulation for evaluating information access systems
With the emergence of various information access systems exhibiting increasing complexity,
there is a critical need for sound and scalable means of automatic evaluation. To address …
there is a critical need for sound and scalable means of automatic evaluation. To address …
[BUCH][B] Bandit algorithms
T Lattimore, C Szepesvári - 2020 - books.google.com
Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …
and the multi-armed bandit model is a commonly used framework to address it. This …
Equity of attention: Amortizing individual fairness in rankings
Rankings of people and items are at the heart of selection-making, match-making, and
recommender systems, ranging from employment sites to sharing economy platforms. As …
recommender systems, ranging from employment sites to sharing economy platforms. As …
End-to-end neural ad-hoc ranking with kernel pooling
This paper proposes K-NRM, a kernel based neural model for document ranking. Given a
query and a set of documents, K-NRM uses a translation matrix that models word-level …
query and a set of documents, K-NRM uses a translation matrix that models word-level …
Controlling fairness and bias in dynamic learning-to-rank
Rankings are the primary interface through which many online platforms match users to
items (eg news, products, music, video). In these two-sided markets, not only the users draw …
items (eg news, products, music, video). In these two-sided markets, not only the users draw …
Unbiased learning-to-rank with biased feedback
Implicit feedback (eg, clicks, dwell times, etc.) is an abundant source of data in human-
interactive systems. While implicit feedback has many advantages (eg, it is inexpensive to …
interactive systems. While implicit feedback has many advantages (eg, it is inexpensive to …
Convolutional neural networks for soft-matching n-grams in ad-hoc search
This paper presents\textttConv-KNRM, a Convolutional Kernel-based Neural Ranking Model
that models n-gram soft matches for ad-hoc search. Instead of exact matching query and …
that models n-gram soft matches for ad-hoc search. Instead of exact matching query and …
Position bias estimation for unbiased learning to rank in personal search
A well-known challenge in learning from click data is its inherent bias and most notably
position bias. Traditional click models aim to extract the‹ query, document› relevance and …
position bias. Traditional click models aim to extract the‹ query, document› relevance and …
Joint multisided exposure fairness for recommendation
Prior research on exposure fairness in the context of recommender systems has focused
mostly on disparities in the exposure of individual or groups of items to individual users of …
mostly on disparities in the exposure of individual or groups of items to individual users of …
A review on individual and multistakeholder fairness in tourism recommender systems
The growing use of Recommender Systems (RS) across various industries, including e-
commerce, social media, news, travel, and tourism, has prompted researchers to examine …
commerce, social media, news, travel, and tourism, has prompted researchers to examine …