Search Results for author: Huifeng Guo

Found 33 papers, 20 papers with code

DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction

8 code implementations12 Apr 2018 Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, Xiuqiang He, Zhenhua Dong

In this paper, we study two instances of DeepFM where its "deep" component is DNN and PNN respectively, for which we denote as DeepFM-D and DeepFM-P. Comprehensive experiments are conducted to demonstrate the effectiveness of DeepFM-D and DeepFM-P over the existing models for CTR prediction, on both benchmark data and commercial data.

Click-Through Rate Prediction Feature Engineering +1

Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data

8 code implementations1 Jul 2018 Yanru Qu, Bohui Fang, Wei-Nan Zhang, Ruiming Tang, Minzhe Niu, Huifeng Guo, Yong Yu, Xiuqiang He

User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search.

Click-Through Rate Prediction Feature Engineering +3

Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction

6 code implementations9 Apr 2019 Bin Liu, Ruiming Tang, Yingzhi Chen, Jinkai Yu, Huifeng Guo, Yuzhou Zhang

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, Deep Session Interest Network(DSIN)

Click-Through Rate Prediction Recommendation Systems

How Can Recommender Systems Benefit from Large Language Models: A Survey

1 code implementation9 Jun 2023 Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Hao Zhang, Yong liu, Chuhan Wu, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, Weinan Zhang

In this paper, we conduct a comprehensive survey on this research direction from the perspective of the whole pipeline in real-world recommender systems.

Ethics Feature Engineering +5

An Embedding Learning Framework for Numerical Features in CTR Prediction

1 code implementation16 Dec 2020 Huifeng Guo, Bo Chen, Ruiming Tang, Weinan Zhang, Zhenguo Li, Xiuqiang He

In this paper, we propose a novel embedding learning framework for numerical features in CTR prediction (AutoDis) with high model capacity, end-to-end training and unique representation properties preserved.

Click-Through Rate Prediction Feature Engineering +1

ScaleFreeCTR: MixCache-based Distributed Training System for CTR Models with Huge Embedding Table

1 code implementation17 Apr 2021 Huifeng Guo, Wei Guo, Yong Gao, Ruiming Tang, Xiuqiang He, Wenzhi Liu

Different from the models with dense training data, the training data for CTR models is usually high-dimensional and sparse.

A Framework for Recommending Accurate and Diverse ItemsUsing Bayesian Graph Convolutional Neural Networks

1 code implementation Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2020 Jianing Sun, Wei Guo, Dengcheng Zhang, Yingxue Zhang, Florence Regol, Yaochen Hu, Huifeng Guo, Ruiming Tang, Han Yuan, Xiuqiang He, Mark Coates

Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize the various relationships between a user and items while modeling a user's preferences.

Recommendation Systems

Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling

5 code implementations29 Oct 2018 Feng Liu, Ruiming Tang, Xutao Li, Wei-Nan Zhang, Yunming Ye, Haokun Chen, Huifeng Guo, Yuzhou Zhang

The DRR framework treats recommendation as a sequential decision making procedure and adopts an "Actor-Critic" reinforcement learning scheme to model the interactions between the users and recommender systems, which can consider both the dynamic adaptation and long-term rewards.

Collaborative Filtering Decision Making +4

IntTower: the Next Generation of Two-Tower Model for Pre-Ranking System

2 code implementations18 Oct 2022 Xiangyang Li, Bo Chen, Huifeng Guo, Jingjie Li, Chenxu Zhu, Xiang Long, Sujian Li, Yichao Wang, Wei Guo, Longxia Mao, JinXing Liu, Zhenhua Dong, Ruiming Tang

FE-Block module performs fine-grained and early feature interactions to capture the interactive signals between user and item towers explicitly and CIR module leverages a contrastive interaction regularization to further enhance the interactions implicitly.

Compressed Interaction Graph based Framework for Multi-behavior Recommendation

1 code implementation4 Mar 2023 Wei Guo, Chang Meng, Enming Yuan, ZhiCheng He, Huifeng Guo, Yingxue Zhang, Bo Chen, Yaochen Hu, Ruiming Tang, Xiu Li, Rui Zhang

However, it is challenging to explore multi-behavior data due to the unbalanced data distribution and sparse target behavior, which lead to the inadequate modeling of high-order relations when treating multi-behavior data ''as features'' and gradient conflict in multitask learning when treating multi-behavior data ''as labels''.

Multi-Task Learning

HAMUR: Hyper Adapter for Multi-Domain Recommendation

1 code implementation12 Sep 2023 Xiaopeng Li, Fan Yan, Xiangyu Zhao, Yichao Wang, Bo Chen, Huifeng Guo, Ruiming Tang

Secondly, due to the distribution differences among domains, the utilization of static parameters in existing methods limits their flexibility to adapt to diverse domains.

OptEmbed: Learning Optimal Embedding Table for Click-through Rate Prediction

1 code implementation9 Aug 2022 Fuyuan Lyu, Xing Tang, Hong Zhu, Huifeng Guo, Yingxue Zhang, Ruiming Tang, Xue Liu

To this end, we propose an optimal embedding table learning framework OptEmbed, which provides a practical and general method to find an optimal embedding table for various base CTR models.

Click-Through Rate Prediction Recommendation Systems

Diffusion Augmentation for Sequential Recommendation

1 code implementation22 Sep 2023 Qidong Liu, Fan Yan, Xiangyu Zhao, Zhaocheng Du, Huifeng Guo, Ruiming Tang, Feng Tian

However, sequential recommendation often faces the problem of data sparsity, which widely exists in recommender systems.

Data Augmentation Sequential Recommendation

Helen: Optimizing CTR Prediction Models with Frequency-wise Hessian Eigenvalue Regularization

1 code implementation23 Feb 2024 Zirui Zhu, Yong liu, Zangwei Zheng, Huifeng Guo, Yang You

We explore the typical data characteristics and optimization statistics of CTR prediction, revealing a strong positive correlation between the top hessian eigenvalue and feature frequency.

Click-Through Rate Prediction

Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks

1 code implementation26 Oct 2022 Hengyu Zhang, Enming Yuan, Wei Guo, ZhiCheng He, Jiarui Qin, Huifeng Guo, Bo Chen, Xiu Li, Ruiming Tang

Sequential recommendation (SR) plays an important role in personalized recommender systems because it captures dynamic and diverse preferences from users' real-time increasing behaviors.

Disentanglement Information Retrieval +1

ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems

2 code implementations19 Mar 2024 Pengyue Jia, Yejing Wang, Zhaocheng Du, Xiangyu Zhao, Yichao Wang, Bo Chen, Wanyu Wang, Huifeng Guo, Ruiming Tang

Secondly, the existing literature's lack of detailed analysis on selection attributes, based on large-scale datasets and a thorough comparison among selection techniques and DRS backbones, restricts the generalizability of findings and impedes deployment on DRS.

Benchmarking feature selection +1

GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems

1 code implementation25 Aug 2020 Yishi Xu, Yingxue Zhang, Wei Guo, Huifeng Guo, Ruiming Tang, Mark Coates

We develop a Graph Structure Aware Incremental Learning framework, GraphSAIL, to address the commonly experienced catastrophic forgetting problem that occurs when training a model in an incremental fashion.

Incremental Learning Recommendation Systems

A Unified Framework for Multi-Domain CTR Prediction via Large Language Models

1 code implementation17 Dec 2023 Zichuan Fu, Xiangyang Li, Chuhan Wu, Yichao Wang, Kuicai Dong, Xiangyu Zhao, Mengchen Zhao, Huifeng Guo, Ruiming Tang

Click-Through Rate (CTR) prediction is a crucial task in online recommendation platforms as it involves estimating the probability of user engagement with advertisements or items by clicking on them.

Click-Through Rate Prediction Language Modelling +2

Multi-Graph Convolution Collaborative Filtering

no code implementations1 Jan 2020 Jianing Sun, Yingxue Zhang, Chen Ma, Mark Coates, Huifeng Guo, Ruiming Tang, Xiuqiang He

In this work, we develop a graph convolution-based recommendation framework, named Multi-Graph Convolution Collaborative Filtering (Multi-GCCF), which explicitly incorporates multiple graphs in the embedding learning process.

Collaborative Filtering

A Practical Incremental Method to Train Deep CTR Models

no code implementations4 Sep 2020 Yichao Wang, Huifeng Guo, Ruiming Tang, Zhirong Liu, Xiuqiang He

Deep learning models in recommender systems are usually trained in the batch mode, namely iteratively trained on a fixed-size window of training data.

Incremental Learning Recommendation Systems

Dual Graph enhanced Embedding Neural Network for CTR Prediction

no code implementations1 Jun 2021 Wei Guo, Rong Su, Renhao Tan, Huifeng Guo, Yingxue Zhang, Zhirong Liu, Ruiming Tang, Xiuqiang He

To solve these problems, we propose a novel module named Dual Graph enhanced Embedding, which is compatible with various CTR prediction models to alleviate these two problems.

Click-Through Rate Prediction Recommendation Systems

Content Filtering Enriched GNN Framework for News Recommendation

no code implementations25 Oct 2021 Yong Gao, Huifeng Guo, Dandan Lin, Yingxue Zhang, Ruiming Tang, Xiuqiang He

It is compatible with existing GNN-based approaches for news recommendation and can capture both collaborative and content filtering information simultaneously.

Collaborative Filtering News Recommendation

MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction

no code implementations30 Nov 2021 Wei Guo, Can Zhang, ZhiCheng He, Jiarui Qin, Huifeng Guo, Bo Chen, Ruiming Tang, Xiuqiang He, Rui Zhang

With the help of two novel CNN-based multi-interest extractors, self-supervision signals are discovered with full considerations of different interest representations (point-wise and union-wise), interest dependencies (short-range and long-range), and interest correlations (inter-item and intra-item).

Click-Through Rate Prediction Contrastive Learning +3

A Comprehensive Survey on Automated Machine Learning for Recommendations

no code implementations4 Apr 2022 Bo Chen, Xiangyu Zhao, Yejing Wang, Wenqi Fan, Huifeng Guo, Ruiming Tang

Deep recommender systems (DRS) are critical for current commercial online service providers, which address the issue of information overload by recommending items that are tailored to the user's interests and preferences.

AutoML BIG-bench Machine Learning +2

Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction

no code implementations12 Dec 2022 Shiwei Li, Huifeng Guo, Lu Hou, Wei zhang, Xing Tang, Ruiming Tang, Rui Zhang, Ruixuan Li

To this end, we formulate a novel quantization training paradigm to compress the embeddings from the training stage, termed low-precision training (LPT).

Click-Through Rate Prediction Quantization

Multi-Task Deep Recommender Systems: A Survey

no code implementations7 Feb 2023 Yuhao Wang, Ha Tsz Lam, Yi Wong, Ziru Liu, Xiangyu Zhao, Yichao Wang, Bo Chen, Huifeng Guo, Ruiming Tang

Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual improvement among tasks considering their shared knowledge.

Multi-Task Learning Recommendation Systems +1

AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation

no code implementations14 Aug 2023 Ziru Liu, Kecheng Chen, Fengyi Song, Bo Chen, Xiangyu Zhao, Huifeng Guo, Ruiming Tang

In the domain of streaming recommender systems, conventional methods for addressing new user IDs or item IDs typically involve assigning initial ID embeddings randomly.

Recommendation Systems

Time-aligned Exposure-enhanced Model for Click-Through Rate Prediction

no code implementations19 Aug 2023 Hengyu Zhang, Chang Meng, Wei Guo, Huifeng Guo, Jieming Zhu, Guangpeng Zhao, Ruiming Tang, Xiu Li

Click-Through Rate (CTR) prediction, crucial in applications like recommender systems and online advertising, involves ranking items based on the likelihood of user clicks.

Click-Through Rate Prediction Recommendation Systems

Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation

no code implementations5 Sep 2023 Jingtong Gao, Bo Chen, Menghui Zhu, Xiangyu Zhao, Xiaopeng Li, Yuhao Wang, Yichao Wang, Huifeng Guo, Ruiming Tang

To address these limitations, we propose a Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendations (HierRec), which perceives implicit patterns adaptively and conducts explicit and implicit scenario modeling jointly.

Click-Through Rate Prediction

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