Search Results for author: Ruiming Tang

Found 59 papers, 28 papers with code

Dynamically Expandable Graph Convolution for Streaming Recommendation

1 code implementation21 Mar 2023 Bowei He, Xu He, Yingxue Zhang, Ruiming Tang, Chen Ma

Personalized recommender systems have been widely studied and deployed to reduce information overload and satisfy users' diverse needs.

Graph Learning Recommendation Systems

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

A Survey on User Behavior Modeling in Recommender Systems

no code implementations22 Feb 2023 ZhiCheng He, Weiwen Liu, Wei Guo, Jiarui Qin, Yingxue Zhang, Yaochen Hu, Ruiming Tang

Besides, we elaborate on the industrial practices of UBM methods with the hope of providing insights into the application value of existing UBM solutions.

Recommendation Systems

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

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

A Bird's-eye View of Reranking: from List Level to Page Level

1 code implementation17 Nov 2022 Yunjia Xi, Jianghao Lin, Weiwen Liu, Xinyi Dai, Weinan Zhang, Rui Zhang, Ruiming Tang, Yong Yu

Moreover, simply applying a shared network for all the lists fails to capture the commonalities and distinctions in user behaviors on different lists.

Recommendation Systems

Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation

no code implementations11 Nov 2022 Haolun Wu, Yingxue Zhang, Chen Ma, Wei Guo, Ruiming Tang, Xue Liu, Mark Coates

To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations.

Decision Making Recommendation Systems +2

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

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

1 code implementation18 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.

A Brief History of Recommender Systems

no code implementations5 Sep 2022 Zhenhua Dong, Zhe Wang, Jun Xu, Ruiming Tang, JiRong Wen

Soon after the invention of the Internet, the recommender system emerged and related technologies have been extensively studied and applied by both academia and industry.

Recommendation Systems

Task Aligned Meta-learning based Augmented Graph for Cold-Start Recommendation

no code implementations11 Aug 2022 Yuxiang Shi, Yue Ding, Bo Chen, YuYang Huang, Ruiming Tang, Dong Wang

In this paper, we propose a Task aligned Meta-learning based Augmented Graph (TMAG) to address cold-start recommendation.

Meta-Learning Recommendation Systems

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

Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation

no code implementations3 Aug 2022 Chang Meng, Ziqi Zhao, Wei Guo, Yingxue Zhang, Haolun Wu, Chen Gao, Dong Li, Xiu Li, Ruiming Tang

More specifically, we propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning (CKML) framework to learn shared and behavior-specific interests for different behaviors.

Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation

1 code implementation2 Aug 2022 Haolun Wu, Chen Ma, Yingxue Zhang, Xue Liu, Ruiming Tang, Mark Coates

In order to effectively utilize such information, most research adopts the pairwise ranking method on constructed training triplets (user, positive item, negative item) and aims to distinguish between positive items and negative items for each user.

Bilevel Optimization

An F-shape Click Model for Information Retrieval on Multi-block Mobile Pages

1 code implementation17 Jun 2022 Lingyue Fu, Jianghao Lin, Weiwen Liu, Ruiming Tang, Weinan Zhang, Rui Zhang, Yong Yu

However, with the development of user interface (UI) design, the layout of displayed items on a result page tends to be multi-block (i. e., multi-list) style instead of a single list, which requires different assumptions to model user behaviors more accurately.

Information Retrieval Retrieval

Cross Pairwise Ranking for Unbiased Item Recommendation

1 code implementation26 Apr 2022 Qi Wan, Xiangnan He, Xiang Wang, Jiancan Wu, Wei Guo, Ruiming Tang

In this work, we develop a new learning paradigm named Cross Pairwise Ranking (CPR) that achieves unbiased recommendation without knowing the exposure mechanism.

Recommendation Systems

Multi-Level Interaction Reranking with User Behavior History

1 code implementation20 Apr 2022 Yunjia Xi, Weiwen Liu, Jieming Zhu, Xilong Zhao, Xinyi Dai, Ruiming Tang, Weinan Zhang, Rui Zhang, Yong Yu

MIR combines low-level cross-item interaction and high-level set-to-list interaction, where we view the candidate items to be reranked as a set and the users' behavior history in chronological order as a list.

Recommendation Systems

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 +1

PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation

no code implementations23 Mar 2022 Yi Li, Jieming Zhu, Weiwen Liu, Liangcai Su, Guohao Cai, Qi Zhang, Ruiming Tang, Xi Xiao, Xiuqiang He

Specifically, PEAR not only captures feature-level and item-level interactions, but also models item contexts from both the initial ranking list and the historical clicked item list.

Recommendation Systems Re-Ranking

Neural Re-ranking in Multi-stage Recommender Systems: A Review

1 code implementation14 Feb 2022 Weiwen Liu, Yunjia Xi, Jiarui Qin, Fei Sun, Bo Chen, Weinan Zhang, Rui Zhang, Ruiming Tang

As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects user experience and satisfaction by rearranging the input ranking lists, and thereby plays a critical role in MRS. With the advances in deep learning, neural re-ranking has become a trending topic and been widely applied in industrial applications.

Recommendation Systems Re-Ranking

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

QA4PRF: A Question Answering based Framework for Pseudo Relevance Feedback

no code implementations16 Nov 2021 Handong Ma, Jiawei Hou, Chenxu Zhu, Weinan Zhang, Ruiming Tang, Jincai Lai, Jieming Zhu, Xiuqiang He, Yong Yu

Pseudo relevance feedback (PRF) automatically performs query expansion based on top-retrieved documents to better represent the user's information need so as to improve the search results.

Question Answering Semantic Similarity +1

AIM: Automatic Interaction Machine for Click-Through Rate Prediction

1 code implementation5 Nov 2021 Chenxu Zhu, Bo Chen, Weinan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, Zhenguo Li, Yong Yu

To address these three issues mentioned above, we propose Automatic Interaction Machine (AIM) with three core components, namely, Feature Interaction Search (FIS), Interaction Function Search (IFS) and Embedding Dimension Search (EDS), to select significant feature interactions, appropriate interaction functions and necessary embedding dimensions automatically in a unified framework.

Click-Through Rate Prediction

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

Context-aware Reranking with Utility Maximization for Recommendation

no code implementations18 Oct 2021 Yunjia Xi, Weiwen Liu, Xinyi Dai, Ruiming Tang, Weinan Zhang, Qing Liu, Xiuqiang He, Yong Yu

As a critical task for large-scale commercial recommender systems, reranking has shown the potential of improving recommendation results by uncovering mutual influence among items.

Graph Attention Recommendation Systems

Extracting Attentive Social Temporal Excitation for Sequential Recommendation

no code implementations28 Sep 2021 Yunzhe Li, Yue Ding, Bo Chen, Xin Xin, Yule Wang, Yuxiang Shi, Ruiming Tang, Dong Wang

In this paper, we propose a novel time-aware sequential recommendation framework called Social Temporal Excitation Networks (STEN), which introduces temporal point processes to model the fine-grained impact of friends' behaviors on the user s dynamic interests in an event-level direct paradigm.

Collaborative Filtering Graph Embedding +2

Retrieval & Interaction Machine for Tabular Data Prediction

1 code implementation11 Aug 2021 Jiarui Qin, Weinan Zhang, Rong Su, Zhirong Liu, Weiwen Liu, Ruiming Tang, Xiuqiang He, Yong Yu

Prediction over tabular data is an essential task in many data science applications such as recommender systems, online advertising, medical treatment, etc.

Click-Through Rate Prediction Recommendation Systems +1

Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning

no code implementations25 Jun 2021 Weiwen Liu, Feng Liu, Ruiming Tang, Ben Liao, Guangyong Chen, Pheng Ann Heng

Fairness in recommendation has attracted increasing attention due to bias and discrimination possibly caused by traditional recommenders.

Fairness Recommendation Systems +2

AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction

no code implementations9 Jun 2021 Xiangli Yang, Qing Liu, Rong Su, Ruiming Tang, Zhirong Liu, Xiuqiang He

The field-wise transfer policy decides how the pre-trained embedding representations are frozen or fine-tuned based on the given instance from the target domain.

Click-Through Rate Prediction Recommendation Systems +1

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

Deep Learning for Click-Through Rate Estimation

no code implementations21 Apr 2021 Weinan Zhang, Jiarui Qin, Wei Guo, Ruiming Tang, Xiuqiang He

In this survey, we provide a comprehensive review of deep learning models for CTR estimation tasks.

Recommendation Systems

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.

An Adversarial Imitation Click Model for Information Retrieval

1 code implementation13 Apr 2021 Xinyi Dai, Jianghao Lin, Weinan Zhang, Shuai Li, Weiwen Liu, Ruiming Tang, Xiuqiang He, Jianye Hao, Jun Wang, Yong Yu

Modern information retrieval systems, including web search, ads placement, and recommender systems, typically rely on learning from user feedback.

Imitation Learning Information Retrieval +2

Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation

no code implementations13 Jan 2021 Chen Ma, Liheng Ma, Yingxue Zhang, Ruiming Tang, Xue Liu, Mark Coates

Personalized recommender systems are playing an increasingly important role as more content and services become available and users struggle to identify what might interest them.

Metric Learning Recommendation Systems

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

U-rank: Utility-oriented Learning to Rank with Implicit Feedback

no code implementations1 Nov 2020 Xinyi Dai, Jiawei Hou, Qing Liu, Yunjia Xi, Ruiming Tang, Weinan Zhang, Xiuqiang He, Jun Wang, Yong Yu

To this end, we propose a novel ranking framework called U-rank that directly optimizes the expected utility of the ranking list.

Click-Through Rate Prediction Learning-To-Rank +1

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

GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems

no code implementations25 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 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

Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning

no code implementations18 Jun 2020 Sijin Zhou, Xinyi Dai, Haokun Chen, Wei-Nan Zhang, Kan Ren, Ruiming Tang, Xiuqiang He, Yong Yu

Interactive recommender system (IRS) has drawn huge attention because of its flexible recommendation strategy and the consideration of optimal long-term user experiences.

Decision Making Recommendation Systems +3

Personalized Re-ranking for Improving Diversity in Live Recommender Systems

no code implementations14 Apr 2020 Yichao Wang, Xiangyu Zhang, Zhirong Liu, Zhenhua Dong, Xinhua Feng, Ruiming Tang, Xiuqiang He

To overcome such limitation, our re-ranking model proposes a personalized DPP to model the trade-off between accuracy and diversity for each individual user.

Recommendation Systems Re-Ranking

AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction

4 code implementations25 Mar 2020 Bin Liu, Chenxu Zhu, Guilin Li, Wei-Nan Zhang, Jincai Lai, Ruiming Tang, Xiuqiang He, Zhenguo Li, Yong Yu

By implementing a regularized optimizer over the architecture parameters, the model can automatically identify and remove the redundant feature interactions during the training process of the model.

Click-Through Rate Prediction Recommendation Systems

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

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

5 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

Large-scale Interactive Recommendation with Tree-structured Policy Gradient

no code implementations14 Nov 2018 Haokun Chen, Xinyi Dai, Han Cai, Wei-Nan Zhang, Xuejian Wang, Ruiming Tang, Yuzhou Zhang, Yong Yu

Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance.

Recommendation Systems Reinforcement Learning (RL)

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

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

7 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

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

5 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

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