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 …
Learning elastic embeddings for customizing on-device recommenders
In today's context, deploying data-driven services like recommendation on edge devices
instead of cloud servers becomes increasingly attractive due to privacy and network latency …
instead of cloud servers becomes increasingly attractive due to privacy and network latency …
DSpar: An embarrassingly simple strategy for efficient GNN training and inference via degree-based sparsification
Running Graph Neural Networks (GNNs) on large graphs suffers from notoriously
inefficiency. This is attributed to the sparse graph-based operations, which is hard to be …
inefficiency. This is attributed to the sparse graph-based operations, which is hard to be …
Feature representation learning for click-through rate prediction: A review and new perspectives
Representation learning has been a critical topic in machine learning. In Click-through Rate
Prediction, most features are represented as embedding vectors and learned …
Prediction, most features are represented as embedding vectors and learned …
Introducing lenskit-auto, an experimental automated recommender system (autorecsys) toolkit
LensKit is one of the first and most popular Recommender System libraries. While LensKit
offers a wide variety of features, it does not include any optimization strategies or guidelines …
offers a wide variety of features, it does not include any optimization strategies or guidelines …
NAS-CTR: efficient neural architecture search for click-through rate prediction
Click-Through Rate (CTR) prediction has been widely used in many machine learning tasks
such as online advertising and personalization recommendation. Unfortunately, given a …
such as online advertising and personalization recommendation. Unfortunately, given a …
A general method for automatic discovery of powerful interactions in click-through rate prediction
Modeling powerful interactions is a critical challenge in Click-through rate (CTR) prediction,
which is one of the most typical machine learning tasks in personalized advertising and …
which is one of the most typical machine learning tasks in personalized advertising and …
A low-code tool supporting the development of recommender systems
The design of recommender systems (RSs) to support software development encompasses
the fulfillment of different steps, including data preprocessing, choice of the most appropriate …
the fulfillment of different steps, including data preprocessing, choice of the most appropriate …
On the generalizability and predictability of recommender systems
D McElfresh, S Khandagale… - Advances in …, 2022 - proceedings.neurips.cc
While other areas of machine learning have seen more and more automation, designing a
high-performing recommender system still requires a high level of human effort …
high-performing recommender system still requires a high level of human effort …
Advancing Automation of Design Decisions in Recommender System Pipelines
T Vente - Proceedings of the 17th ACM Conference on …, 2023 - dl.acm.org
Recommender systems have become essential in domains like streaming services, social
media platforms, and e-commerce websites. However, the development of a recommender …
media platforms, and e-commerce websites. However, the development of a recommender …