Click-Through Rate Prediction with Neural Recommender Systems
CTR prediction plays a crucial role in digital advertising and recommender systems, by determining the likelihood of a user clicking on an ad or recommendation. Early models often fall short in capturing complex nonlinear relationships and interactions between users and items.
By focusing on neural recommender systems, we aim to contribute to the ongoing evolution of CTR prediction methodologies, offering insights and practical solutions to the challenges faced by the industry.
The dataset has been provided by Yuval David6. (2023). BGU - recommendation systems click through rate. Kaggle. https://kaggle.com/competitions/bgu-recommendation-systems-click-through-rate
Made in collaboration with Kevin Antony, Bonnie Liu, Nisha McNealis, and Siddharth Nandy for the Advanced Data Mining capstone project.