Intelligent video surveillance: a review through deep learning techniques for crowd analysis
Big data applications are consuming most of the space in industry and research area.
Among the widespread examples of big data, the role of video streams from CCTV cameras …
Among the widespread examples of big data, the role of video streams from CCTV cameras …
Click-through rate prediction in online advertising: A literature review
Y Yang, P Zhai - Information Processing & Management, 2022 - Elsevier
Predicting the probability that a user will click on a specific advertisement has been a
prevalent issue in online advertising, attracting much research attention in the past decades …
prevalent issue in online advertising, attracting much research attention in the past decades …
Autofis: Automatic feature interaction selection in factorization models for click-through rate prediction
Learning feature interactions is crucial for click-through rate (CTR) prediction in
recommender systems. In most existing deep learning models, feature interactions are either …
recommender systems. In most existing deep learning models, feature interactions are either …
Fi-gnn: Modeling feature interactions via graph neural networks for ctr prediction
Click-through rate (CTR) prediction is an essential task in web applications such as online
advertising and recommender systems, whose features are usually in multi-field form. The …
advertising and recommender systems, whose features are usually in multi-field form. The …
BaGFN: broad attentive graph fusion network for high-order feature interactions
Modeling feature interactions is of crucial significance to high-quality feature engineering on
multifiled sparse data. At present, a series of state-of-the-art methods extract cross features …
multifiled sparse data. At present, a series of state-of-the-art methods extract cross features …
Deep learning for click-through rate estimation
Click-through rate (CTR) estimation plays as a core function module in various personalized
online services, including online advertising, recommender systems, and web search etc …
online services, including online advertising, recommender systems, and web search etc …
Adaptive factorization network: Learning adaptive-order feature interactions
Various factorization-based methods have been proposed to leverage second-order, or
higher-order cross features for boosting the performance of predictive models. They …
higher-order cross features for boosting the performance of predictive models. They …
CAN: feature co-action network for click-through rate prediction
W Bian, K Wu, L Ren, Q Pi, Y Zhang, C **ao… - Proceedings of the …, 2022 - dl.acm.org
Feature interaction has been recognized as an important problem in machine learning,
which is also very essential for click-through rate (CTR) prediction tasks. In recent years …
which is also very essential for click-through rate (CTR) prediction tasks. In recent years …
Map: A model-agnostic pretraining framework for click-through rate prediction
With the widespread application of online advertising systems, click-through rate (CTR)
prediction has received more and more attention and research. The most prominent features …
prediction has received more and more attention and research. The most prominent features …
Personalized re-ranking for recommendation
Ranking is a core task in recommender systems, which aims at providing an ordered list of
items to users. Typically, a ranking function is learned from the labeled dataset to optimize …
items to users. Typically, a ranking function is learned from the labeled dataset to optimize …