Click-Through Rate Prediction Models based on Interest Modeling
Author:
Affiliation:
1. University College London, UK
2. University of Hong Kong, China
3. Polytechnic University of Hong Kong, China
4. University of Southampton, UK
5. Beijing Normal University, China and BNU-HKBU United International College, China
Funder
Guangdong College Enhancement and Innovation Program
Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College
UIC research grant
natural science foundation of China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3640872.3640876
Reference21 articles.
1. KRITARTH BISHT. 2022. Stacking Ensemble Strategy for Click Through Rate Prediction. Ph. D. Dissertation.
2. Wide & Deep Learning for Recommender Systems
3. Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search
4. Yufei Feng, Fuyu Lv, Weichen Shen, Menghan Wang, Fei Sun, Yu Zhu, and Keping Yang. 2019. Deep session interest network for click-through rate prediction. arXiv preprint arXiv:1905.06482 (2019).
5. Alex Graves and Jürgen Schmidhuber. 2005. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural networks 18, 5-6 (2005), 602–610.
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