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 …
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 …
One model to serve all: Star topology adaptive recommender for multi-domain ctr prediction
Traditional industry recommendation systems usually use data in a single domain to train
models and then serve the domain. However, a large-scale commercial platform often …
models and then serve the domain. However, a large-scale commercial platform often …
Pepnet: Parameter and embedding personalized network for infusing with personalized prior information
J Chang, C Zhang, Y Hui, D Leng, Y Niu… - Proceedings of the 29th …, 2023 - dl.acm.org
With the increase of content pages and interactive buttons in online services such as online-
shopping and video-watching websites, industrial-scale recommender systems face …
shopping and video-watching websites, industrial-scale recommender systems face …
Kuairand: an unbiased sequential recommendation dataset with randomly exposed videos
Recommender systems deployed in real-world applications can have inherent exposure
bias, which leads to the biased logged data plaguing the researchers. A fundamental way to …
bias, which leads to the biased logged data plaguing the researchers. A fundamental way to …
Bars: Towards open benchmarking for recommender systems
The past two decades have witnessed the rapid development of personalized
recommendation techniques. Despite the significant progress made in both research and …
recommendation techniques. Despite the significant progress made in both research and …
Generative adversarial framework for cold-start item recommendation
The cold-start problem has been a long-standing issue in recommendation. Embedding-
based recommendation models provide recommendations by learning embeddings for each …
based recommendation models provide recommendations by learning embeddings for each …
Rethinking" batch" in batchnorm
BatchNorm is a critical building block in modern convolutional neural networks. Its unique
property of operating on" batches" instead of individual samples introduces significantly …
property of operating on" batches" instead of individual samples introduces significantly …
Multi-factor sequential re-ranking with perception-aware diversification
Feed recommendation systems, which recommend a sequence of items for users to browse
and interact with, have gained significant popularity in practical applications. In feed …
and interact with, have gained significant popularity in practical applications. In feed …
Deconfounding duration bias in watch-time prediction for video recommendation
Watch-time prediction remains to be a key factor in reinforcing user engagement via video
recommendations. It has become increasingly important given the ever-growing popularity …
recommendations. It has become increasingly important given the ever-growing popularity …