Results 41 to 50 of about 1,080,302 (365)
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
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
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
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
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
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
Addressing Fast Changing Fashion Trends in Multi-Stage Recommender Systems
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2023 Fashion industry is driven by fashion cycles, in which a fashion item is launched, rises to mainstream appeal and becomes a trend, then diminishes and eventually becomes obsolete. These properties make it critical to incorporate temporal information when
Aayush Singha Roy +3 more
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
An Alternative Cross Entropy Loss for Learning-to-Rank
, 2021 Listwise learning-to-rank methods form a powerful class of ranking algorithms that are widely adopted in applications such as information retrieval. These algorithms learn to rank a set of items by optimizing a loss that is a function of the entire set --
Bruch, Sebastian
core +1 more source