[图书][B] Click models for web search
With the rapid growth of web search in recent years the problem of modeling its users has
started to attract more and more attention of the information retrieval community. This has …
started to attract more and more attention of the information retrieval community. This has …
Hybrid online–offline learning to rank using simulated annealing strategy based on dependent click model
OAS Ibrahim, EMG Younis - Knowledge and Information Systems, 2022 - Springer
Learning to rank (LTR) is the process of constructing a model for ranking documents or
objects. It is useful for many applications such as Information retrieval (IR) and …
objects. It is useful for many applications such as Information retrieval (IR) and …
Multileave gradient descent for fast online learning to rank
Modern search systems are based on dozens or even hundreds of ranking features. The
dueling bandit gradient descent (DBGD) algorithm has been shown to effectively learn …
dueling bandit gradient descent (DBGD) algorithm has been shown to effectively learn …
Multileaved comparisons for fast online evaluation
Evaluation methods for information retrieval systems come in three types: offline evaluation,
using static data sets annotated for relevance by human judges; user studies, usually …
using static data sets annotated for relevance by human judges; user studies, usually …
Marginal-certainty-aware fair ranking algorithm
Ranking systems are ubiquitous in modern Internet services, including online marketplaces,
social media, and search engines. Traditionally, ranking systems only focus on how to get …
social media, and search engines. Traditionally, ranking systems only focus on how to get …
Variance reduction in gradient exploration for online learning to rank
Online Learning to Rank (OL2R) algorithms learn from implicit user feedback on the fly. The
key to such algorithms is an unbiased estimate of gradients, which is often (trivially) …
key to such algorithms is an unbiased estimate of gradients, which is often (trivially) …
Online learning to rank for information retrieval: Sigir 2016 tutorial
A Grotov, M De Rijke - Proceedings of the 39th International ACM SIGIR …, 2016 - dl.acm.org
During the past 10--15 years offline learning to rank has had a tremendous influence on
information retrieval, both scientifically and in practice. Recently, as the limitations of offline …
information retrieval, both scientifically and in practice. Recently, as the limitations of offline …
MergeRUCB: A method for large-scale online ranker evaluation
A key challenge in information retrieval is that of on-line ranker evaluation: determining
which one of a finite set of rankers performs the best in expectation on the basis of user …
which one of a finite set of rankers performs the best in expectation on the basis of user …
Mitigating exploitation bias in learning to rank with an uncertainty-aware empirical bayes approach
Ranking is at the core of many artificial intelligence (AI) applications, including search
engines, recommender systems, etc. Modern ranking systems are often constructed with …
engines, recommender systems, etc. Modern ranking systems are often constructed with …
Efficient exploration of gradient space for online learning to rank
H Wang, R Langley, S Kim, E McCord-Snook… - The 41st international …, 2018 - dl.acm.org
Online learning to rank (OL2R) optimizes the utility of returned search results based on
implicit feedback gathered directly from users. In this paper, we accelerate the online …
implicit feedback gathered directly from users. In this paper, we accelerate the online …