Results 31 to 40 of about 1,101,099 (365)
Unbiased Learning-to-Rank with Biased Feedback [PDF]
Web Search and Data Mining, 2016 Implicit feedback (e.g., clicks, dwell times, etc.) is an abundant source of data in human-interactive systems. While implicit feedback has many advantages (e.g., it is inexpensive to collect, user centric, and timely), its inherent biases are a key ...
T. Joachims +2 more
semanticscholar +1 more source
Correcting for Selection Bias in Learning-to-rank Systems [PDF]
The Web Conference, 2020 Click data collected by modern recommendation systems are an important source of observational data that can be utilized to train learning-to-rank (LTR) systems.
Zohreh Ovaisi +4 more
semanticscholar +1 more source
PairRank: Online Pairwise Learning to Rank by Divide-and-Conquer [PDF]
The Web Conference, 2021 Online Learning to Rank (OL2R) eliminates the need of explicit relevance annotation by directly optimizing the rankers from their interactions with users.
Yiling Jia +3 more
semanticscholar +1 more source
Leveraging Unlabeled Data for Crowd Counting by Learning to Rank [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 We propose a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework. To induce a ranking of cropped images, we use the observation that any sub-image of a crowded scene image is ...
Xialei Liu +2 more
semanticscholar +1 more source
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022 Using implicit feedback collected from user clicks as training labels for learning-to-rank algorithms is a well-developed paradigm that has been extensively studied and used in modern IR systems.
Tao Yang +5 more
semanticscholar +1 more source
Interactive Learning of Pattern Rankings [PDF]
International Journal on Artificial Intelligence Tools, 2014 Pattern mining provides useful tools for exploratory data analysis. Numerous efficient algorithms exist that are able to discover various types of patterns in large datasets. Unfortunately, the problem of identifying patterns that are genuinely interesting to a particular user remains challenging. Current approaches generally require considerable data
Dzyuba, Vladimir +3 more
openaire +3 more sources
Fairness in Ranking, Part II: Learning-to-Rank and Recommender Systems
ACM Computing Surveys, 2022 In the past few years, there has been much work on incorporating fairness requirements into algorithmic rankers, with contributions coming from the data management, algorithms, information retrieval, and recommender systems communities.
Meike Zehlike, Ke Yang, J. Stoyanovich
semanticscholar +1 more source
Boosting the Learning for Ranking Patterns
Algorithms, 2023 Pattern mining is a valuable tool for exploratory data analysis, but identifying relevant patterns for a specific user is challenging. Various interestingness measures have been developed to evaluate patterns, but they may not efficiently estimate user ...
Nassim Belmecheri +4 more
doaj +1 more source
Improving Query Quality for Transductive Learning in Learning to Rank
IEEE Access, 2020 In traditional transductive learning, all queries are used in learning to rank in order to generate pseudo-labels when sufficient training data are not available. However, low quality queries may affect retrieval performance in transductive learning.
Xin Zhang, Zhi Cheng
doaj +1 more source
Learning to rank using privileged information [PDF]
, 2013 Many computer vision problems have an asymmetric distribution of information between training and test time. In this work, we study the case where we are given additional information about the training data, which however will not be available at test ...
Lampert, Christoph H +2 more
core +3 more sources