1 code implementation • 18 Mar 2024 • Jun Lei, Yuxi Zhou, Xue Tian, Qinghao Zhao, Qi Zhang, Shijia Geng, Qingbo Wu, Shenda Hong
By employing 150 beats for information fusion decision algorithm, the average AUC can reach 0. 7591.
no code implementations • 9 Dec 2023 • Qi Liu, Xuyang Hou, Defu Lian, Zhe Wang, Haoran Jin, Jia Cheng, Jun Lei
Most existing methods focus on the network architecture design of the CTR model for better accuracy and suffer from the data sparsity problem.
no code implementations • 15 Nov 2023 • Qi Liu, Xuyang Hou, Haoran Jin, Jin Chen, Zhe Wang, Defu Lian, Tan Qu, Jia Cheng, Jun Lei
The insights from this subset reveal the user's decision-making process related to the candidate item, improving prediction accuracy.
no code implementations • 5 Sep 2023 • Shaohua Liu, Yu Qi, Gen Li, Mingjian Chen, Teng Zhang, Jia Cheng, Jun Lei
Specifically, we construct subgraphs of spatial, temporal, spatial-temporal, and global views respectively to precisely characterize the user's interests in various contexts.
no code implementations • 11 Aug 2023 • Xuyang Hou, Zhe Wang, Qi Liu, Tan Qu, Jia Cheng, Jun Lei
Many works focus on user behavior modeling to improve CTR prediction performance.
no code implementations • 27 Jun 2023 • Jun Lei, Ji-Qian Zhao, Jing-Qi Wang, An-Bao Xu
The main idea of our method is improving the tensor leverage sampling strategy and introduce tensor QR decomposition into tensor completion.
2 code implementations • 29 Oct 2022 • Yifang Qin, Yifan Wang, Fang Sun, Wei Ju, Xuyang Hou, Zhe Wang, Jia Cheng, Jun Lei, Ming Zhang
Point-of-Interest (POI) recommendation plays a vital role in various location-aware services.
no code implementations • 5 May 2022 • Xin Chen, Qingtao Tang, Ke Hu, Yue Xu, Shihang Qiu, Jia Cheng, Jun Lei
In Meituan, one of the largest e-commerce platform in China, an item is typically displayed with its image and whether a user clicks the item or not is usually influenced by its image, which implies that user's image behaviors are helpful for understanding user's visual preference and improving the accuracy of CTR prediction.
no code implementations • 18 Jan 2022 • Ke Hu, Yi Qi, Jianqiang Huang, Jia Cheng, Jun Lei
To address this problem, we formulate CTR prediction as a continual learning task and propose COLF, a hybrid COntinual Learning Framework for CTR prediction, which has a memory-based modular architecture that is designed to adapt, learn and give predictions continuously when faced with non-stationary drifting click data streams.
1 code implementation • 25 Nov 2021 • Jin Xu, Mingjian Chen, Jianqiang Huang, Xingyuan Tang, Ke Hu, Jian Li, Jia Cheng, Jun Lei
Graph Neural Networks (GNNs) have become increasingly popular and achieved impressive results in many graph-based applications.
1 code implementation • 10 Jun 2021 • Jianqiang Huang, Ke Hu, Qingtao Tang, Mingjian Chen, Yi Qi, Jia Cheng, Jun Lei
Click-through rate (CTR) prediction plays an important role in online advertising and recommender systems.