Results 41 to 50 of about 1,101,099 (365)
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
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
Proceedings of the 23rd International Conference on World Wide Web, 2014 User-generated content is a growing source of valuable information and its analysis can lead to a better understanding of the users needs and trends. In this paper, we leverage user feedback about YouTube videos for the task of affective video ranking.
Orellana-Rodriguez, Claudia +3 more
openaire +3 more sources
Reinforcement Learning to Rank [PDF]
Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2019 Interactive systems such as search engines or recommender systems are increasingly moving away from single-turn exchanges with users. Instead, series of exchanges between the user and the system are becoming mainstream, especially when users have complex needs or when the system struggles to understand the user's intent.
openaire +3 more sources
Policy-Aware Unbiased Learning to Rank for Top-k Rankings [PDF]
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020 Counterfactual Learning to Rank (LTR) methods optimize ranking systems using logged user interactions that contain interaction biases. Existing methods are only unbiased if users are presented with all relevant items in every ranking.
Harrie Oosterhuis, M. D. Rijke
semanticscholar +1 more source
Learning to Rank Reviewers for Pull Requests
IEEE Access, 2019 In pull-based software development, anyone who wants to contribute to a project can request integration of the code changes to the public repository by sending a pull request to the development team.
Xin Ye
doaj +1 more source
Cognitive biomarker prioritization in Alzheimer’s Disease using brain morphometric data
BMC Medical Informatics and Decision Making, 2020 Background Cognitive assessments represent the most common clinical routine for the diagnosis of Alzheimer’s Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, it is important to determine a proper set of ...
Bo Peng +6 more
doaj +1 more source
Learning to Rank for Multi-Step Ahead Time-Series Forecasting
IEEE Access, 2021 Time-series forecasting is a fundamental problem associated with a wide range of engineering, financial, and social applications. The challenge arises from the complexity due to the time-variant property of time series and the inevitable diminishing ...
Jiuding Duan, Hisashi Kashima
doaj +1 more source
Siamese-Network-Based Learning to Rank for No-Reference 2D and 3D Image Quality Assessment
IEEE Access, 2019 2D image quality assessment (IQA) and stereoscopic 3D IQA are considered as two different tasks in the literature. In this paper, we present an index for both no-reference 2D and 3D IQA.
Yuzhen Niu +3 more
doaj +1 more source
Learning-to-Rank with BERT in TF-Ranking
, 2020 This paper describes a machine learning algorithm for document (re)ranking, in which queries and documents are firstly encoded using BERT [1], and on top of that a learning-to-rank (LTR) model constructed with TF-Ranking (TFR) [2] is applied to further optimize the ranking performance.
Han, Shuguang +3 more
openaire +2 more sources