no code implementations • 28 Feb 2022 • Jingwei Zhuo, Bin Liu, Xiang Li, Han Zhu, Xiaoqiang Zhu
Motivated by the observation that model-free methods like behavioral retargeting (BR) and item-based collaborative filtering (ItemCF) hit different parts of the user-item relevance compared to neural sequential recommendation models, we propose a novel model-agnostic training approach called WSLRec, which adopts a three-stage framework: pre-training, top-$k$ mining, and fine-tuning.
no code implementations • 22 Sep 2021 • Daqing Chang, Jintao Liu, Ziru Xu, Han Li, Han Zhu, Xiaoqiang Zhu
Vertically, a parent fusion layer is designed in M to transmit the user preference representation in higher levels of T to the current level, grasping the essence that tree-based methods are generating the candidate set from coarse to detail during the beam search retrieval.
1 code implementation • 11 Jun 2021 • Chao Wen, Miao Xu, Zhilin Zhang, Zhenzhe Zheng, Yuhui Wang, Xiangyu Liu, Yu Rong, Dong Xie, Xiaoyang Tan, Chuan Yu, Jian Xu, Fan Wu, Guihai Chen, Xiaoqiang Zhu, Bo Zheng
Third, to deploy MAAB in the large-scale advertising system with millions of advertisers, we propose a mean-field approach.
no code implementations • 7 Jun 2021 • Xiangyu Liu, Chuan Yu, Zhilin Zhang, Zhenzhe Zheng, Yu Rong, Hongtao Lv, Da Huo, YiQing Wang, Dagui Chen, Jian Xu, Fan Wu, Guihai Chen, Xiaoqiang Zhu
In e-commerce advertising, it is crucial to jointly consider various performance metrics, e. g., user experience, advertiser utility, and platform revenue.
no code implementations • 25 May 2021 • Liyi Guo, Junqi Jin, Haoqi Zhang, Zhenzhe Zheng, Zhiye Yang, Zhizhuang Xing, Fei Pan, Lvyin Niu, Fan Wu, Haiyang Xu, Chuan Yu, Yuning Jiang, Xiaoqiang Zhu
To achieve this goal, the advertising platform needs to identify the advertiser's optimization objectives, and then recommend the corresponding strategies to fulfill the objectives.
1 code implementation • 29 Apr 2021 • Siyu Gu, Xiang-Rong Sheng, Ying Fan, Guorui Zhou, Xiaoqiang Zhu
If conversion happens outside the waiting window, this sample will be duplicated and ingested into the training pipeline with a positive label.
no code implementations • 3 Mar 2021 • Xun Yang, Yunli Wang, Cheng Chen, Qing Tan, Chuan Yu, Jian Xu, Xiaoqiang Zhu
On the other hand, the response time of these systems is strictly limited to a short period, e. g. 300 milliseconds in our real system, which is also being exhausted by the increasingly complex models and algorithms.
no code implementations • 18 Feb 2021 • Jin Li, Jie Liu, Shangzhou Li, Yao Xu, Ran Cao, Qi Li, Biye Jiang, Guan Wang, Han Zhu, Kun Gai, Xiaoqiang Zhu
When receiving a user request, matching system (i) finds the crowds that the user belongs to; (ii) retrieves all ads that have targeted those crowds.
no code implementations • 27 Jan 2021 • Xiang-Rong Sheng, Liqin Zhao, Guorui Zhou, Xinyao Ding, Binding Dai, Qiang Luo, Siran Yang, Jingshan Lv, Chi Zhang, Hongbo Deng, Xiaoqiang Zhu
Concretely, STAR has the star topology, which consists of the shared centered parameters and domain-specific parameters.
1 code implementation • 25 Nov 2020 • Chao Du, Zhifeng Gao, Shuo Yuan, Lining Gao, Ziyan Li, Yifan Zeng, Xiaoqiang Zhu, Jian Xu, Kun Gai, Kuang-Chih Lee
In this paper, we propose a novel Deep Uncertainty-Aware Learning (DUAL) method to learn CTR models based on Gaussian processes, which can provide predictive uncertainty estimations while maintaining the flexibility of deep neural networks.
no code implementations • 11 Nov 2020 • Weijie Bian, Kailun Wu, Lejian Ren, Qi Pi, Yujing Zhang, Can Xiao, Xiang-Rong Sheng, Yong-Nan Zhu, Zhangming Chan, Na Mou, Xinchen Luo, Shiming Xiang, Guorui Zhou, Xiaoqiang Zhu, Hongbo Deng
For example, a simple attempt to learn the combination of feature A and feature B <A, B> as the explicit cartesian product representation of new features can outperform previous implicit feature interaction models including factorization machine (FM)-based models and their variations.
2 code implementations • 31 Jul 2020 • Zhe Wang, Liqin Zhao, Biye Jiang, Guorui Zhou, Xiaoqiang Zhu, Kun Gai
We name it COLD (Computing power cost-aware Online and Lightweight Deep pre-ranking system).
no code implementations • 17 Jun 2020 • Biye Jiang, Pengye Zhang, Rihan Chen, Binding Dai, Xinchen Luo, Yin Yang, Guan Wang, Guorui Zhou, Xiaoqiang Zhu, Kun Gai
These stages usually allocate resource manually with specific computing power budgets, which requires the serving configuration to adapt accordingly.
1 code implementation • 10 Jun 2020 • Pi Qi, Xiaoqiang Zhu, Guorui Zhou, Yujing Zhang, Zhe Wang, Lejian Ren, Ying Fan, Kun Gai
Serving the main traffic in our real system now, SIM models user behavior data with maximum length reaching up to 54000, pushing SOTA to 54x.
1 code implementation • 30 Apr 2020 • Jiarui Jin, Yuchen Fang, Wei-Nan Zhang, Kan Ren, Guorui Zhou, Jian Xu, Yong Yu, Jun Wang, Xiaoqiang Zhu, Kun Gai
Position bias is a critical problem in information retrieval when dealing with implicit yet biased user feedback data.
no code implementations • 12 Jul 2019 • Zheng Gao, Lin Guo, Chi Ma, Xiao Ma, Kai Sun, Hang Xiang, Xiaoqiang Zhu, Hongsong Li, Xiaozhong Liu
Anomaly detection is facing with emerging challenges in many important industry domains, such as cyber security and online recommendation and advertising.
no code implementations • 25 Jun 2019 • Guorui Zhou, Kailun Wu, Weijie Bian, Zhao Yang, Xiaoqiang Zhu, Kun Gai
In this paper, we model user behavior using an interest delay model, study carefully the embedding mechanism, and obtain two important results: (i) We theoretically prove that small aggregation radius of embedding vectors of items which belongs to a same user interest domain will result in good generalization performance of deep CTR model.
2 code implementations • 22 May 2019 • Qi Pi, Weijie Bian, Guorui Zhou, Xiaoqiang Zhu, Kun Gai
To our knowledge, this is one of the first industrial solutions that are capable of handling long sequential user behavior data with length scaling up to thousands.
1 code implementation • 2 May 2019 • Kan Ren, Jiarui Qin, Yuchen Fang, Wei-Nan Zhang, Lei Zheng, Weijie Bian, Guorui Zhou, Jian Xu, Yong Yu, Xiaoqiang Zhu, Kun Gai
In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user.
15 code implementations • 11 Sep 2018 • Guorui Zhou, Na Mou, Ying Fan, Qi Pi, Weijie Bian, Chang Zhou, Xiaoqiang Zhu, Kun Gai
Easy-to-use, Modular and Extendible package of deep-learning based CTR models. DeepFM, DeepInterestNetwork(DIN), DeepInterestEvolutionNetwork(DIEN), DeepCrossNetwork(DCN), AttentionalFactorizationMachine(AFM), Neural Factorization Machine(NFM), AutoInt
Ranked #1 on Click-Through Rate Prediction on Amazon Dataset
6 code implementations • 21 Apr 2018 • Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, Kun Gai
To the best of our knowledge, this is the first public dataset which contains samples with sequential dependence of click and conversion labels for CVR modeling.
no code implementations • 17 Nov 2017 • Tiezheng Ge, Liqin Zhao, Guorui Zhou, Keyu Chen, Shuying Liu, Huimin Yi, Zelin Hu, Bochao Liu, Peng Sun, Haoyu Liu, Pengtao Yi, Sui Huang, Zhiqiang Zhang, Xiaoqiang Zhu, Yu Zhang, Kun Gai
So we propose to model user preference jointly with user behavior ID features and behavior images.
2 code implementations • 14 Aug 2017 • Guorui Zhou, Ying Fan, Runpeng Cui, Weijie Bian, Xiaoqiang Zhu, Kun Gai
Models applied on real time response task, like click-through rate (CTR) prediction model, require high accuracy and rigorous response time.
17 code implementations • 21 Jun 2017 • Guorui Zhou, Chengru Song, Xiaoqiang Zhu, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, Kun Gai
In this way, user features are compressed into a fixed-length representation vector, in regardless of what candidate ads are.
Ranked #1 on Click-Through Rate Prediction on Amazon
3 code implementations • 18 Apr 2017 • Kun Gai, Xiaoqiang Zhu, Han Li, Kai Liu, Zhe Wang
CTR prediction in real-world business is a difficult machine learning problem with large scale nonlinear sparse data.