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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
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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
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
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 Learning Curves
, 2020 Many automated machine learning methods, such as those for hyperparameter and neural architecture optimization, are computationally expensive because they involve training many different model configurations. In this work, we present a new method that saves computational budget by terminating poor configurations early on in the training. In contrast to
Wistuba, Martin, Pedapati, Tejaswini
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Simple to Complex Cross-modal Learning to Rank [PDF]
, 1998 The heterogeneity-gap between different modalities brings a significant challenge to multimedia information retrieval. Some studies formalize the cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal embedding space to measure ...
Luo, Minnan +5 more
core +3 more sources
Image Retrieval Based on Learning to Rank and Multiple Loss
ISPRS International Journal of Geo-Information, 2019 Image retrieval applying deep convolutional features has achieved the most advanced performance in most standard benchmark tests. In image retrieval, deep metric learning (DML) plays a key role and aims to capture semantic similarity information carried ...
Lili Fan +4 more
doaj +1 more source
Learning to Rank for Educational Search Engines
IEEE Transactions on Learning Technologies, 2021 In this digital age, there is an abundance of online educational materials in public and proprietary platforms. To allow effective retrieval of educational resources, it is a necessity to build keyword-based search engines over these collections.
Arif Usta +3 more
semanticscholar +1 more source
Extracting Emotion Causes Using Learning to Rank Methods From an Information Retrieval Perspective
IEEE Access, 2019 Emotion cause extraction is a challenging task for the fine-grained emotion analysis. Even though a few studies have addressed the task using clause-level classification methods, most of them have partly ignored emotion-level context information.
Bo Xu +5 more
doaj +1 more source
Fair pairwise learning to rank [PDF]
2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), 2020 Ranking algorithms based on Neural Networks have been a topic of recent research. Ranking is employed in everyday applications like product recommendations, search results, or even in finding good candidates for hiring. However, Neural Networks are mostly opaque tools, and it is hard to evaluate why a specific candidate, for instance, was not ...
Mattia Cerrato +4 more
openaire +3 more sources