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Replication Data: Neural Signals of Video Advertisement Liking

Creators: Hang-Yee Chan
Publication Date: Jul 30, 2023
Creators: Hang-Yee Chan

This dataset, hosted on the Open Science Framework (OSF), contains the replication data for the study “Neural Signals of Video Advertisement Liking: Insights into Psychological Processes and Their Temporal Dynamics” (Chan et al., 2024, Journal of Marketing Research). It includes neuroimaging (fMRI) and behavioural data used to examine the neural correlates of advertisement liking, identifying the psychological processes and their temporal dynamics that underlie consumer responses to video advertising.

Advertisement CTR Prediction Data

Creators: Huawei
Publication Date: 2020
Creators: Huawei

Advertisement CTR prediction is the key problem in the area of computing advertising. Increasing the accuracy of Advertisement CTR prediction is critical to improve the effectiveness of precision marketing. In this competition, we release big advertising datasets that are anonymized. Based on the datasets, contestants are required to build Advertisement CTR prediction models. The aim of the event is to find talented individuals to promote the development of Advertisement CTR prediction algorithms. The datasets contain the advertising behavior data collected from seven consecutive days, including a training dataset and a testing dataset. The total size of the datasets amounts to 6,86 GB. It contains millions of observations and is structured into training and testing sets, with multiple variables capturing different aspects of user-ad interactions. These variables include user identifiers, ad identifiers, timestamps, user behavior features, and ad content features, allowing researchers to analyze engagement patterns and develop predictive models for ad click-through rates. This dataset is valuable for improving advertising strategies and refining targeted marketing approaches.

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