Search Results for author: Guorui Zhou

Found 22 papers, 12 papers with code

End-to-end training of Multimodal Model and ranking Model

2 code implementations9 Apr 2024 Xiuqi Deng, Lu Xu, Xiyao Li, Jinkai Yu, Erpeng Xue, Zhongyuan Wang, Di Zhang, Zhaojie Liu, Guorui Zhou, Yang song, Na Mou, Shen Jiang, Han Li

In this paper, we propose an industrial multimodal recommendation framework named EM3: End-to-end training of Multimodal Model and ranking Model, which sufficiently utilizes multimodal information and allows personalized ranking tasks to directly train the core modules in the multimodal model to obtain more task-oriented content features, without overburdening resource consumption.

Contrastive Learning Multimodal Recommendation

Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model

no code implementations5 Apr 2024 Xinrun Du, Zhouliang Yu, Songyang Gao, Ding Pan, Yuyang Cheng, Ziyang Ma, Ruibin Yuan, Xingwei Qu, Jiaheng Liu, Tianyu Zheng, Xinchen Luo, Guorui Zhou, Binhang Yuan, Wenhu Chen, Jie Fu, Ge Zhang

In this study, we introduce CT-LLM, a 2B large language model (LLM) that illustrates a pivotal shift towards prioritizing the Chinese language in developing LLMs.

Language Modelling Large Language Model

Ensure Timeliness and Accuracy: A Novel Sliding Window Data Stream Paradigm for Live Streaming Recommendation

no code implementations22 Feb 2024 Fengqi Liang, Baigong Zheng, Liqin Zhao, Guorui Zhou, Qian Wang, Yanan Niu

In this paper, we propose a new data stream design paradigm, dubbed Sliver, that addresses the timeliness and accuracy problem of labels by reducing the window size and implementing a sliding window correspondingly.

Recommendation Systems

ContentCTR: Frame-level Live Streaming Click-Through Rate Prediction with Multimodal Transformer

no code implementations26 Jun 2023 Jiaxin Deng, Dong Shen, Shiyao Wang, Xiangyu Wu, Fan Yang, Guorui Zhou, Gaofeng Meng

However, most previous works treat the live as a whole item and explore the Click-through-Rate (CTR) prediction framework on item-level, neglecting that the dynamic changes that occur even within the same live room.

Click-Through Rate Prediction Dynamic Time Warping +1

Multi-behavior Self-supervised Learning for Recommendation

1 code implementation22 May 2023 Jingcao Xu, Chaokun Wang, Cheng Wu, Yang song, Kai Zheng, Xiaowei Wang, Changping Wang, Guorui Zhou, Kun Gai

Secondly, existing methods utilizing self-supervised learning (SSL) to tackle the data sparsity neglect the serious optimization imbalance between the SSL task and the target task.

Recommendation Systems Self-Supervised Learning

Instant Representation Learning for Recommendation over Large Dynamic Graphs

1 code implementation22 May 2023 Cheng Wu, Chaokun Wang, Jingcao Xu, Ziwei Fang, Tiankai Gu, Changping Wang, Yang song, Kai Zheng, Xiaowei Wang, Guorui Zhou

Furthermore, the Neighborhood Disturbance existing in dynamic graphs deteriorates the performance of neighbor-aggregation based graph models.

Recommendation Systems Representation Learning

An End-to-End Framework for Marketing Effectiveness Optimization under Budget Constraint

no code implementations9 Feb 2023 Ziang Yan, Shusen Wang, Guorui Zhou, Jingjian Lin, Peng Jiang

Recent advances in this field often address the budget allocation problem using a two-stage paradigm: the first stage estimates the individual-level treatment effects using causal inference algorithms, and the second stage invokes integer programming techniques to find the optimal budget allocation solution.

Causal Inference Marketing

Real Negatives Matter: Continuous Training with Real Negatives for Delayed Feedback Modeling

1 code implementation29 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.

CAN: Feature Co-Action for Click-Through Rate Prediction

no code implementations11 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.

Click-Through Rate Prediction

COLD: Towards the Next Generation of Pre-Ranking System

2 code implementations31 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).

Recommendation Systems

DCAF: A Dynamic Computation Allocation Framework for Online Serving System

no code implementations17 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.

Recommendation Systems Retrieval

A Deep Recurrent Survival Model for Unbiased Ranking

1 code implementation30 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.

Information Retrieval Position +2

Res-embedding for Deep Learning Based Click-Through Rate Prediction Modeling

no code implementations25 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.

Click-Through Rate Prediction

Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction

2 code implementations22 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.

Click-Through Rate Prediction Recommendation Systems

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

1 code implementation2 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.


Deep Interest Evolution Network for Click-Through Rate Prediction

15 code implementations11 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

Click-Through Rate Prediction

Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net

2 code implementations14 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.

Click-Through Rate Prediction

Deep Interest Network for Click-Through Rate Prediction

17 code implementations21 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.

Click-Through Rate Prediction

Hierarchical Latent Semantic Mapping for Automated Topic Generation

no code implementations11 Nov 2015 Guorui Zhou, Guang Chen

Inspired by these algorithms, in this paper, we propose a novel method named Hierarchical Latent Semantic Mapping (HLSM), which automatically generates topics from corpus.

Community Detection

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