Marketing
129 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Marketing
| Trend | Dataset | Best Model | Paper | Code | Compare |
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BIG-bench
Most implemented papers
Survival regression with accelerated failure time model in XGBoost
In this work, we implement loss functions for learning accelerated failure time (AFT) models in XGBoost, to increase the support for survival modeling for different kinds of label censoring.
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.
Deep learning for Background Replacement in Video Conferencing
kiranshahi/Real-time-Background-replacement-in-Video-Conferencing
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International Journal of Network Dynamics and Intelligence 2023
Background replacement is one of the most used features in video conferencing applications by many people, perhaps mainly for privacy protection, but also for other purposes such as branding, marketing and promoting professionalism.
Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction
RunlongYu/CELS
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KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2023
Inspired by natural evolution, we propose a general Cognitive EvoLutionary Search (CELS) framework, where cognitive ability refers to the malleability of organisms to orientate to the environment.
On Learning from Label Proportions
Learning from Label Proportions (LLP) is a learning setting, where the training data is provided in groups, or "bags", and only the proportion of each class in each bag is known.
Online Model Evaluation in a Large-Scale Computational Advertising Platform
Effective and reliable evaluation of an online bidding model is crucial for making faster model improvements as well as for utilizing the marketing budgets more efficiently.
Strategyproof Peer Selection using Randomization, Partitioning, and Apportionment
Peer reviews, evaluations, and selections are a fundamental aspect of modern science.
Online Influence Maximization in Non-Stationary Social Networks
Nevertheless, the existing studies mostly investigate the problem on a one-off basis, assuming fixed known influence probabilities among users, or the knowledge of the exact social network topology.
Cost-aware Targeted Viral Marketing in billion-scale networks
In this paper, we propose a new problem, called Cost-aware Targeted Viral Marketing (CTVM), to find the most cost-effective seed users who can influence the most relevant users to the advertisement.
Concordance and the Smallest Covering Set of Preference Orderings
Preference orderings are orderings of a set of items according to the preferences (of judges).
