1 code implementation • COLING 2022 • Weijie Yu, Liang Pang, Jun Xu, Bing Su, Zhenhua Dong, Ji-Rong Wen
Enjoying the partial transport properties of OPT, the selected key sentences can not only effectively enhance the matching accuracy, but also be explained as the rationales for the matching results.
no code implementations • COLING 2022 • Chen Xu, Jun Xu, Zhenhua Dong, Ji-Rong Wen
In this paper, we formalize the task of semantic sentence matching as a problem of graph matching in which each sentence is represented as a directed graph according to its syntactic structures.
1 code implementation • Findings (ACL) 2022 • Jian Li, Jieming Zhu, Qiwei Bi, Guohao Cai, Lifeng Shang, Zhenhua Dong, Xin Jiang, Qun Liu
Accurately matching user’s interests and candidate news is the key to news recommendation.
1 code implementation • 13 Feb 2025 • Jun Yuan, Guohao Cai, Zhenhua Dong
To bridge this gap, we propose a novel Contextual-Aware Position Encoding method for sequential recommendation, abbreviated as CAPE.
1 code implementation • 11 Feb 2025 • Xiaopeng Ye, Chen Xu, Zhongxiang Sun, Jun Xu, Gang Wang, Zhenhua Dong, Ji-Rong Wen
While previous studies have attempted to model creator behavior, they often overlook the role of information asymmetry.
no code implementations • 10 Feb 2025 • Longtao Xiao, Haozhao Wang, Cheng Wang, Linfei Ji, Yifan Wang, Jieming Zhu, Zhenhua Dong, Rui Zhang, Ruixuan Li
In the second stage, we propose an in-modality knowledge distillation task, designed to effectively capture and integrate knowledge from both semantic and collaborative modalities.
no code implementations • 9 Feb 2025 • Jiyuan Ren, Zhaocheng Du, Zhihao Wen, Qinglin Jia, Sunhao Dai, Chuhan Wu, Zhenhua Dong
To solve this, we introduce SynAlign: a synthetic data generation and filtering framework based on key attribute distribution matching.
no code implementations • 16 Jan 2025 • Nuo Chen, Quanyu Dai, Xiaoyu Dong, Xiao-Ming Wu, Zhenhua Dong
Conversational recommender systems (CRS) involve both recommendation and dialogue tasks, which makes their evaluation a unique challenge.
no code implementations • 18 Dec 2024 • Jie-Jing Shao, Xiao-Wen Yang, Bo-Wen Zhang, Baizhi Chen, Wen-Da Wei, Guohao Cai, Zhenhua Dong, Lan-Zhe Guo, Yu-Feng Li
Recent advances in LLMs, particularly in language reasoning and tool integration, have rapidly sparked the real-world development of Language Agents.
1 code implementation • 15 Dec 2024 • Zhuo Wu, Qinglin Jia, Chuhan Wu, Zhaocheng Du, Shuai Wang, Zan Wang, Zhenhua Dong
More specifically, for each sample we use LLM to generate a user profile description based on user behavior history or off-the-shelf profile features, which is used to guide LLM to play the role of this user and evaluate the relative preference for two recommendation results generated by different models.
no code implementations • 8 Oct 2024 • Jun Yuan, Guohao Cai, Zhenhua Dong
Training all tasks naively can result in inconsistent learning, highlighting the need for the development of multi-task optimization (MTO) methods to tackle this challenge.
1 code implementation • 30 Sep 2024 • Zeyu Zhang, Quanyu Dai, Luyu Chen, Zeren Jiang, Rui Li, Jieming Zhu, Xu Chen, Yi Xie, Zhenhua Dong, Ji-Rong Wen
LLM-based agents have been widely applied as personal assistants, capable of memorizing information from user messages and responding to personal queries.
no code implementations • 15 Aug 2024 • Yang Yang, Bo Chen, Chenxu Zhu, Menghui Zhu, Xinyi Dai, Huifeng Guo, Muyu Zhang, Zhenhua Dong, Ruiming Tang
Click-Through Rate (CTR) prediction is a fundamental technique for online advertising recommendation and the complex online competitive auction process also brings many difficulties to CTR optimization.
no code implementations • 13 Aug 2024 • Yusheng Lu, Zhaocheng Du, Xiangyang Li, Xiangyu Zhao, Weiwen Liu, Yichao Wang, Huifeng Guo, Ruiming Tang, Zhenhua Dong, Yongrui Duan
And employs expectation maximization to infer the embedded latent profile, minimizing textual noise by fixing the prompt template.
no code implementations • 25 Jun 2024 • Minghui Fang, Shengpeng Ji, Jialong Zuo, Hai Huang, Yan Xia, Jieming Zhu, Xize Cheng, Xiaoda Yang, Wenrui Liu, Gang Wang, Zhenhua Dong, Zhou Zhao
Generative retrieval, which has demonstrated effectiveness in text-to-text retrieval, utilizes a sequence-to-sequence model to directly generate candidate identifiers based on natural language queries.
1 code implementation • 20 Jun 2024 • Ye Wang, Jiahao Xun, Minjie Hong, Jieming Zhu, Tao Jin, Wang Lin, Haoyuan Li, Linjun Li, Yan Xia, Zhou Zhao, Zhenhua Dong
Generative retrieval has recently emerged as a promising approach to sequential recommendation, framing candidate item retrieval as an autoregressive sequence generation problem.
1 code implementation • 12 Jun 2024 • Haiyuan Zhao, Guohao Cai, Jieming Zhu, Zhenhua Dong, Jun Xu, Ji-Rong Wen
In video recommendation, an ongoing effort is to satisfy users' personalized information needs by leveraging their logged watch time.
no code implementations • 28 May 2024 • Yuqi Zhou, Sunhao Dai, Liang Pang, Gang Wang, Zhenhua Dong, Jun Xu, Ji-Rong Wen
How and to what extent the source bias affects the neural recommendation models within feedback loop remains unknown.
1 code implementation • 26 May 2024 • Sunhao Dai, Weihao Liu, Yuqi Zhou, Liang Pang, Rongju Ruan, Gang Wang, Zhenhua Dong, Jun Xu, Ji-Rong Wen
The proliferation of Large Language Models (LLMs) has led to an influx of AI-generated content (AIGC) on the internet, transforming the corpus of Information Retrieval (IR) systems from solely human-written to a coexistence with LLM-generated content.
no code implementations • 25 May 2024 • Xiaopeng Ye, Chen Xu, Jun Xu, Xuyang Xie, Gang Wang, Zhenhua Dong
Previous studies often show that the two sides' needs show different urgency: providers need a relatively long-term exposure demand while users want more short-term and accurate service.
no code implementations • 21 May 2024 • Yuang Zhao, Zhaocheng Du, Qinglin Jia, Linxuan Zhang, Zhenhua Dong, Ruiming Tang
With the increase in the business scale and number of domains in online advertising, multi-domain ad recommendation has become a mainstream solution in the industry.
1 code implementation • 17 May 2024 • Xingmei Wang, Weiwen Liu, Xiaolong Chen, Qi Liu, Xu Huang, Yichao Wang, Xiangyang Li, Yasheng Wang, Zhenhua Dong, Defu Lian, Ruiming Tang
This model-agnostic framework can be equipped with plug-and-play textual features, with item-level alignment enhancing the utilization of external information while maintaining training and inference efficiency.
no code implementations • 11 May 2024 • Jieming Zhu, Chuhan Wu, Rui Zhang, Zhenhua Dong
This tutorial seeks to provide a thorough exploration of the latest advancements and future trajectories in multimodal pretraining and generation techniques within the realm of recommender systems.
no code implementations • 23 Apr 2024 • Jieming Zhu, mengqun Jin, Qijiong Liu, Zexuan Qiu, Zhenhua Dong, Xiu Li
Embedding-based retrieval serves as a dominant approach to candidate item matching for industrial recommender systems.
1 code implementation • 21 Apr 2024 • Zeyu Zhang, Xiaohe Bo, Chen Ma, Rui Li, Xu Chen, Quanyu Dai, Jieming Zhu, Zhenhua Dong, Ji-Rong Wen
Compared with original LLMs, LLM-based agents are featured in their self-evolving capability, which is the basis for solving real-world problems that need long-term and complex agent-environment interactions.
1 code implementation • 17 Apr 2024 • Sunhao Dai, Chen Xu, Shicheng Xu, Liang Pang, Zhenhua Dong, Jun Xu
With the rapid advancements of large language models (LLMs), information retrieval (IR) systems, such as search engines and recommender systems, have undergone a significant paradigm shift.
no code implementations • 15 Apr 2024 • JunJie Huang, Guohao Cai, Jieming Zhu, Zhenhua Dong, Ruiming Tang, Weinan Zhang, Yong Yu
RAR consists of two key sub-modules, which synergistically gather information from a vast pool of look-alike users and recall items, resulting in enriched user representations.
no code implementations • 31 Mar 2024 • Qijiong Liu, Jieming Zhu, Yanting Yang, Quanyu Dai, Zhaocheng Du, Xiao-Ming Wu, Zhou Zhao, Rui Zhang, Zhenhua Dong
Personalized recommendation serves as a ubiquitous channel for users to discover information tailored to their interests.
no code implementations • 8 Mar 2024 • Hai Huang, Yan Xia, Shengpeng Ji, Shulei Wang, Hanting Wang, Jieming Zhu, Zhenhua Dong, Zhou Zhao
The Dual Cross-modal Information Disentanglement (DCID) model, utilizing a unified codebook, shows promising results in achieving fine-grained representation and cross-modal generalization.
no code implementations • 27 Feb 2024 • Yuang Zhao, Chuhan Wu, Qinglin Jia, Hong Zhu, Jia Yan, Libin Zong, Linxuan Zhang, Zhenhua Dong, Muyu Zhang
Accurately predicting the probabilities of user feedback, such as clicks and conversions, is critical for advertisement ranking and bidding.
1 code implementation • 30 Nov 2023 • Liangcai Su, Fan Yan, Jieming Zhu, Xi Xiao, Haoyi Duan, Zhou Zhao, Zhenhua Dong, Ruiming Tang
Two-tower models are a prevalent matching framework for recommendation, which have been widely deployed in industrial applications.
no code implementations • 7 Oct 2023 • Zhenhua Dong, Jieming Zhu, Weiwen Liu, Ruiming Tang
Huawei's vision and mission is to build a fully connected intelligent world.
1 code implementation • 16 Aug 2023 • Haiyuan Zhao, Lei Zhang, Jun Xu, Guohao Cai, Zhenhua Dong, Ji-Rong Wen
In the video recommendation, watch time is commonly adopted as an indicator of user interest.
no code implementations • 19 Jul 2023 • Jiahao Xun, Shengyu Zhang, Yanting Yang, Jieming Zhu, Liqun Deng, Zhou Zhao, Zhenhua Dong, RuiQi Li, Lichao Zhang, Fei Wu
We analyze the CSI task in a disentanglement view with the causal graph technique, and identify the intra-version and inter-version effects biasing the invariant learning.
2 code implementations • 15 Jun 2023 • Jieming Zhu, Guohao Cai, JunJie Huang, Zhenhua Dong, Ruiming Tang, Weinan Zhang
The error memory module is designed with fast access capabilities and undergoes continual refreshing with newly observed data samples during the model serving phase to support fast model adaptation.
4 code implementations • 3 Apr 2023 • Kelong Mao, Jieming Zhu, Liangcai Su, Guohao Cai, Yuru Li, Zhenhua Dong
As such, many two-stream interaction models (e. g., DeepFM and DCN) have been proposed by integrating an MLP network with another dedicated network for enhanced CTR prediction.
Ranked #2 on
Click-Through Rate Prediction
on MovieLens
1 code implementation • 2 Apr 2023 • Qijiong Liu, Jieming Zhu, Jiahao Wu, Tiandeng Wu, Zhenhua Dong, Xiao-Ming Wu
Item list continuation is proposed to model the overall trend of a list and predict subsequent items.
no code implementations • 1 Apr 2023 • Rui Sun, Fengwei Zhou, Zhenhua Dong, Chuanlong Xie, Lanqing Hong, Jiawei Li, Rui Zhang, Zhen Li, Zhenguo Li
By adjusting the perturbation strength in the direction of the paths, our proposed augmentation is controllable and auditable.
no code implementations • 21 Mar 2023 • Dugang Liu, Pengxiang Cheng, Zinan Lin, Xiaolian Zhang, Zhenhua Dong, Rui Zhang, Xiuqiang He, Weike Pan, Zhong Ming
To bridge this gap, we study the debiasing problem from a new perspective and propose to directly minimize the upper bound of an ideal objective function, which facilitates a better potential solution to the system-induced biases.
no code implementations • 1 Mar 2023 • Xu Chen, Jingsen Zhang, Lei Wang, Quanyu Dai, Zhenhua Dong, Ruiming Tang, Rui Zhang, Li Chen, Ji-Rong Wen
To alleviate the above problems, we propose to build an explainable recommendation dataset with multi-aspect real user labeled ground truths.
no code implementations • 12 Nov 2022 • Quanyu Dai, Haoxuan Li, Peng Wu, Zhenhua Dong, Xiao-Hua Zhou, Rui Zhang, Jie Sun
However, in this paper, by theoretically analyzing the bias, variance and generalization bounds of DR methods, we find that existing DR approaches may have poor generalization caused by inaccurate estimation of propensity scores and imputation errors, which often occur in practice.
no code implementations • 25 Oct 2022 • Lei Wang, Xu Chen, Quanyu Dai, Zhenhua Dong
Recommender system has been deployed in a large amount of real-world applications, profoundly influencing people's daily life and production. Traditional recommender models mostly collect as comprehensive as possible user behaviors for accurate preference estimation.
1 code implementation • 20 Oct 2022 • Zhongxiang Sun, Jun Xu, Xiao Zhang, Zhenhua Dong, Ji-Rong Wen
We show that the framework is model-agnostic, and a number of legal case matching models can be applied as the underlying models.
2 code implementations • 18 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.
no code implementations • 5 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.
no code implementations • 15 Aug 2022 • Quanyu Dai, Zhenhua Dong, Xu Chen
Debiased recommender models have recently attracted increasing attention from the academic and industry communities.
1 code implementation • 9 Jul 2022 • Weijie Yu, Zhongxiang Sun, Jun Xu, Zhenhua Dong, Xu Chen, Hongteng Xu, Ji-Rong Wen
As an essential operation of legal retrieval, legal case matching plays a central role in intelligent legal systems.
no code implementations • 9 Jul 2022 • Haoxuan Li, Quanyu Dai, Yuru Li, Yan Lyu, Zhenhua Dong, Xiao-Hua Zhou, Peng Wu
Doubly robust (DR) learning has been studied in many tasks in RS, with the advantage that unbiased learning can be achieved when either a single imputation or a single propensity model is accurate.
no code implementations • 24 Apr 2022 • Guohao Cai, Jieming Zhu, Quanyu Dai, Zhenhua Dong, Xiuqiang He, Ruiming Tang, Rui Zhang
Deep learning-based recommendation has become a widely adopted technique in various online applications.
1 code implementation • 2 Apr 2022 • Haiyuan Zhao, Jun Xu, Xiao Zhang, Guohao Cai, Zhenhua Dong, Ji-Rong Wen
An extension to the pairwise neural ranking is also developed.
no code implementations • 1 Apr 2022 • Zhenlei Wang, Xu Chen, Rui Zhou, Quanyu Dai, Zhenhua Dong, Ji-Rong Wen
The key of sequential recommendation lies in the accurate item correlation modeling.
no code implementations • Findings (ACL) 2022 • Shaobo Li, Xiaoguang Li, Lifeng Shang, Zhenhua Dong, Chengjie Sun, Bingquan Liu, Zhenzhou Ji, Xin Jiang, Qun Liu
We check the words that have three typical associations with the missing words: knowledge-dependent, positionally close, and highly co-occurred.
1 code implementation • 23 Feb 2022 • Yan Lyu, Sunhao Dai, Peng Wu, Quanyu Dai, yuhao deng, Wenjie Hu, Zhenhua Dong, Jun Xu, Shengyu Zhu, Xiao-Hua Zhou
To better support the studies of causal inference and further explanations in recommender systems, we propose a novel semi-synthetic data generation framework for recommender systems where causal graphical models with missingness are employed to describe the causal mechanism of practical recommendation scenarios.
no code implementations • 18 Jan 2022 • Peng Wu, Haoxuan Li, yuhao deng, Wenjie Hu, Quanyu Dai, Zhenhua Dong, Jie Sun, Rui Zhang, Xiao-Hua Zhou
Recently, recommender system (RS) based on causal inference has gained much attention in the industrial community, as well as the states of the art performance in many prediction and debiasing tasks.
no code implementations • 16 Jan 2022 • Mengyue Yang, Guohao Cai, Furui Liu, Zhenhua Dong, Xiuqiang He, Jianye Hao, Jun Wang, Xu Chen
To alleviate these problems, in this paper, we propose a novel debiased recommendation framework based on user feature balancing.
1 code implementation • 26 Sep 2021 • Kelong Mao, Jieming Zhu, Jinpeng Wang, Quanyu Dai, Zhenhua Dong, Xi Xiao, Xiuqiang He
While many existing studies focus on the design of more powerful interaction encoders, the impacts of loss functions and negative sampling ratios have not yet been well explored.
Ranked #4 on
Collaborative Filtering
on Yelp2018
no code implementations • 2 Sep 2021 • Mengyue Yang, Quanyu Dai, Zhenhua Dong, Xu Chen, Xiuqiang He, Jun Wang
To alleviate this problem, in this paper, we propose to reformulate the recommendation task within the causal inference framework, which enables us to counterfactually simulate user ranking-based preferences to handle the data scarce problem.
no code implementations • 5 Mar 2021 • Chang Liu, Xiaoguang Li, Guohao Cai, Zhenhua Dong, Hong Zhu, Lifeng Shang
It is still an open question to leverage various types of information under the BERT framework.
no code implementations • 14 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.
no code implementations • 22 Jan 2020 • Mi Luo, Fei Chen, Pengxiang Cheng, Zhenhua Dong, Xiuqiang He, Jiashi Feng, Zhenguo Li
Recommender systems often face heterogeneous datasets containing highly personalized historical data of users, where no single model could give the best recommendation for every user.
1 code implementation • 3 Dec 2019 • Zifeng Wang, Hong Zhu, Zhenhua Dong, Xiuqiang He, Shao-Lun Huang
In the time of Big Data, training complex models on large-scale data sets is challenging, making it appealing to reduce data volume for saving computation resources by subsampling.
no code implementations • 5 Jul 2019 • Yingtong Dou, Weijian Li, Zhirong Liu, Zhenhua Dong, Jiebo Luo, Philip S. Yu
To the best of our knowledge, this is the first work that investigates the download fraud problem in mobile App markets.
8 code implementations • 12 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.
1 code implementation • 22 Feb 2018 • Fei Chen, Mi Luo, Zhenhua Dong, Zhenguo Li, Xiuqiang He
Statistical and systematic challenges in collaboratively training machine learning models across distributed networks of mobile devices have been the bottlenecks in the real-world application of federated learning.