Search Results for author: Jieming Zhu

Found 60 papers, 34 papers with code

Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation

1 code implementation COLING 2022 Qijiong Liu, Jieming Zhu, Quanyu Dai, Xiaoming Wu

We validate the effectiveness of PREC through both offline evaluation on public datasets and online A/B testing in an industrial application.

Click-Through Rate Prediction News Recommendation

MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal Assistants

1 code implementation30 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.

Diversity Relation Network

STORE: Streamlining Semantic Tokenization and Generative Recommendation with A Single LLM

no code implementations11 Sep 2024 Qijiong Liu, Jieming Zhu, Lu Fan, Zhou Zhao, Xiao-Ming Wu

In this paper, we propose to streamline the semantic tokenization and generative recommendation process with a unified framework, dubbed STORE, which leverages a single large language model (LLM) for both tasks.

Language Modelling Large Language Model +1

Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendation

no code implementations7 Aug 2024 Jiachen Zhu, Jianghao Lin, Xinyi Dai, Bo Chen, Rong Shan, Jieming Zhu, Ruiming Tang, Yong Yu, Weinan Zhang

Thus, LLMs only see a small fraction of the datasets (e. g., less than 10%) instead of the whole datasets, limiting their exposure to the full training space.

Logical Reasoning Recommendation Systems +1

DCNv3: Towards Next Generation Deep Cross Network for CTR Prediction

2 code implementations18 Jul 2024 Honghao Li, Yiwen Zhang, Yi Zhang, Hanwei Li, Lei Sang, Jieming Zhu

Deep & Cross Network and its derivative models have become an important paradigm for click-through rate (CTR) prediction due to their effective balance between computational cost and performance.

Click-Through Rate Prediction

ACE: A Generative Cross-Modal Retrieval Framework with Coarse-To-Fine Semantic Modeling

no code implementations25 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.

Cross-Modal Retrieval Natural Language Queries +2

EAGER: Two-Stream Generative Recommender with Behavior-Semantic Collaboration

1 code implementation20 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.

Retrieval Sequential Recommendation

Counteracting Duration Bias in Video Recommendation via Counterfactual Watch Time

1 code implementation12 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.

counterfactual Recommendation Systems

Multimodal Pretraining and Generation for Recommendation: A Tutorial

no code implementations11 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.

multimodal generation Multimodal Recommendation

Vector Quantization for Recommender Systems: A Review and Outlook

1 code implementation6 May 2024 Qijiong Liu, Xiaoyu Dong, Jiaren Xiao, Nuo Chen, Hengchang Hu, Jieming Zhu, Chenxu Zhu, Tetsuya Sakai, Xiao-Ming Wu

Finally, the survey analyzes the remaining challenges and anticipates future trends in VQ4Rec, including the challenges associated with the training of vector quantization, the opportunities presented by large language models, and emerging trends in multimodal recommender systems.

Feature Compression Quantization +2

CoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendation

no code implementations23 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.

Decoder Language Modelling +3

A Survey on the Memory Mechanism of Large Language Model based Agents

1 code implementation21 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.

Language Modelling Large Language Model

Recall-Augmented Ranking: Enhancing Click-Through Rate Prediction Accuracy with Cross-Stage Data

no code implementations15 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.

Click-Through Rate Prediction

PMG : Personalized Multimodal Generation with Large Language Models

3 code implementations7 Apr 2024 Xiaoteng Shen, Rui Zhang, Xiaoyan Zhao, Jieming Zhu, Xi Xiao

Such user preferences are then fed into a generator, such as a multimodal LLM or diffusion model, to produce personalized content.

multimodal generation Reading Comprehension +1

RAT: Retrieval-Augmented Transformer for Click-Through Rate Prediction

1 code implementation2 Apr 2024 Yushen Li, Jinpeng Wang, Tao Dai, Jieming Zhu, Jun Yuan, Rui Zhang, Shu-Tao Xia

Predicting click-through rates (CTR) is a fundamental task for Web applications, where a key issue is to devise effective models for feature interactions.

Click-Through Rate Prediction Retrieval

Discrete Semantic Tokenization for Deep CTR Prediction

2 code implementations13 Mar 2024 Qijiong Liu, Hengchang Hu, Jiahao Wu, Jieming Zhu, Min-Yen Kan, Xiao-Ming Wu

Incorporating item content information into click-through rate (CTR) prediction models remains a challenge, especially with the time and space constraints of industrial scenarios.

Click-Through Rate Prediction News Recommendation

Unlocking the Potential of Multimodal Unified Discrete Representation through Training-Free Codebook Optimization and Hierarchical Alignment

1 code implementation8 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.

Disentanglement

Benchmarking News Recommendation in the Era of Green AI

1 code implementation7 Mar 2024 Qijiong Liu, Jieming Zhu, Quanyu Dai, Xiao-Ming Wu

Over recent years, news recommender systems have gained significant attention in both academia and industry, emphasizing the need for a standardized benchmark to evaluate and compare the performance of these systems.

Benchmarking News Recommendation +1

Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendation

1 code implementation30 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.

Retrieval

Cross-modal Prompts: Adapting Large Pre-trained Models for Audio-Visual Downstream Tasks

1 code implementation NeurIPS 2023 Haoyi Duan, Yan Xia, Mingze Zhou, Li Tang, Jieming Zhu, Zhou Zhao

This mechanism leverages audio and visual modalities as soft prompts to dynamically adjust the parameters of pre-trained models based on the current multi-modal input features.

Only Encode Once: Making Content-based News Recommender Greener

no code implementations27 Aug 2023 Qijiong Liu, Jieming Zhu, Quanyu Dai, Xiao-Ming Wu

Large pretrained language models (PLM) have become de facto news encoders in modern news recommender systems, due to their strong ability in comprehending textual content.

News Recommendation Recommendation Systems +1

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

DisCover: Disentangled Music Representation Learning for Cover Song Identification

no code implementations19 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.

Blocking Cover song identification +3

Exploiting Counter-Examples for Active Learning with Partial labels

no code implementations14 Jul 2023 Fei Zhang, Yunjie Ye, Lei Feng, Zhongwen Rao, Jieming Zhu, Marcus Kalander, Chen Gong, Jianye Hao, Bo Han

In this setting, an oracle annotates the query samples with partial labels, relaxing the oracle from the demanding accurate labeling process.

Active Learning

Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models

1 code implementation19 Jun 2023 Yunjia Xi, Weiwen Liu, Jianghao Lin, Xiaoling Cai, Hong Zhu, Jieming Zhu, Bo Chen, Ruiming Tang, Weinan Zhang, Rui Zhang, Yong Yu

In this work, we propose an Open-World Knowledge Augmented Recommendation Framework with Large Language Models, dubbed KAR, to acquire two types of external knowledge from LLMs -- the reasoning knowledge on user preferences and the factual knowledge on items.

Music Recommendation Recommendation Systems +1

ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop

2 code implementations15 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.

Recommendation Systems

Denoising Multi-modal Sequential Recommenders with Contrastive Learning

no code implementations3 May 2023 Dong Yao, Shengyu Zhang, Zhou Zhao, Jieming Zhu, Wenqiao Zhang, Rui Zhang, Xiaofei He, Fei Wu

In contrast, modalities that do not cause users' behaviors are potential noises and might mislead the learning of a recommendation model.

Contrastive Learning Denoising +2

FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction

4 code implementations3 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.

Click-Through Rate Prediction feature selection +1

FANS: Fast Non-Autoregressive Sequence Generation for Item List Continuation

1 code implementation2 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.

CCL4Rec: Contrast over Contrastive Learning for Micro-video Recommendation

no code implementations17 Aug 2022 Shengyu Zhang, Bofang Li, Dong Yao, Fuli Feng, Jieming Zhu, Wenyan Fan, Zhou Zhao, Xiaofei He, Tat-Seng Chua, Fei Wu

Micro-video recommender systems suffer from the ubiquitous noises in users' behaviors, which might render the learned user representation indiscriminating, and lead to trivial recommendations (e. g., popular items) or even weird ones that are far beyond users' interests.

Contrastive Learning Recommendation Systems

BARS: Towards Open Benchmarking for Recommender Systems

5 code implementations19 May 2022 Jieming Zhu, Quanyu Dai, Liangcai Su, Rong Ma, Jinyang Liu, Guohao Cai, Xi Xiao, Rui Zhang

Despite significant progress made in both research and practice of recommender systems, to date, there is a lack of a widely-recognized benchmarking standard in this field.

Benchmarking Click-Through Rate Prediction +1

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

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

Contrastive Learning with Positive-Negative Frame Mask for Music Representation

no code implementations17 Mar 2022 Dong Yao, Zhou Zhao, Shengyu Zhang, Jieming Zhu, Yudong Zhu, Rui Zhang, Xiuqiang He

We devise a novel contrastive learning objective to accommodate both self-augmented positives/negatives sampled from the same music.

Contrastive Learning Cover song identification +2

Wnet: Audio-Guided Video Object Segmentation via Wavelet-Based Cross-Modal Denoising Networks

1 code implementation CVPR 2022 Wenwen Pan, Haonan Shi, Zhou Zhao, Jieming Zhu, Xiuqiang He, Zhigeng Pan, Lianli Gao, Jun Yu, Fei Wu, Qi Tian

Audio-Guided video semantic segmentation is a challenging problem in visual analysis and editing, which automatically separates foreground objects from background in a video sequence according to the referring audio expressions.

Decoder Denoising +4

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

Cross-Batch Negative Sampling for Training Two-Tower Recommenders

no code implementations28 Oct 2021 Jinpeng Wang, Jieming Zhu, Xiuqiang He

The two-tower architecture has been widely applied for learning item and user representations, which is important for large-scale recommender systems.

Recommendation Systems Vocal Bursts Valence Prediction

UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation

2 code implementations28 Oct 2021 Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, Xiuqiang He

In this paper, we take one step further to propose an ultra-simplified formulation of GCNs (dubbed UltraGCN), which skips infinite layers of message passing for efficient recommendation.

Collaborative Filtering Recommendation Systems

SimpleX: A Simple and Strong Baseline for Collaborative Filtering

1 code implementation26 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.

Collaborative Filtering Recommendation Systems

Why Do We Click: Visual Impression-aware News Recommendation

1 code implementation26 Sep 2021 Jiahao Xun, Shengyu Zhang, Zhou Zhao, Jieming Zhu, Qi Zhang, Jingjie Li, Xiuqiang He, Xiaofei He, Tat-Seng Chua, Fei Wu

In this work, inspired by the fact that users make their click decisions mostly based on the visual impression they perceive when browsing news, we propose to capture such visual impression information with visual-semantic modeling for news recommendation.

Decision Making News Recommendation

Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding

no code implementations NeurIPS 2020 Zhu Zhang, Zhou Zhao, Zhijie Lin, Jieming Zhu, Xiuqiang He

Weakly-supervised vision-language grounding aims to localize a target moment in a video or a specific region in an image according to the given sentence query, where only video-level or image-level sentence annotations are provided during training.

Contrastive Learning counterfactual +2

Ensemble Knowledge Distillation for CTR Prediction

no code implementations8 Nov 2020 Jieming Zhu, Jinyang Liu, Weiqi Li, Jincai Lai, Xiuqiang He, Liang Chen, Zibin Zheng

Recently, deep learning-based models have been widely studied for click-through rate (CTR) prediction and lead to improved prediction accuracy in many industrial applications.

Click-Through Rate Prediction Knowledge Distillation

BARS-CTR: Open Benchmarking for Click-Through Rate Prediction

6 code implementations12 Sep 2020 Jieming Zhu, Jinyang Liu, Shuai Yang, Qi Zhang, Xiuqiang He

We have publicly released the benchmarking code, evaluation protocols, and hyper-parameter settings of our work to promote reproducible research in this field.

Benchmarking Click-Through Rate Prediction +1

Loghub: A Large Collection of System Log Datasets for AI-driven Log Analytics

8 code implementations14 Aug 2020 Shilin He, Jieming Zhu, Pinjia He, Michael R. Lyu

To fill this significant gap and facilitate more research on AI-driven log analytics, we have collected and released loghub, a large collection of system log datasets.

Software Engineering

Logzip: Extracting Hidden Structures via Iterative Clustering for Log Compression

1 code implementation24 Sep 2019 Jinyang Liu, Jieming Zhu, Shilin He, Pinjia He, Zibin Zheng, Michael R. Lyu

Data compression is essential to reduce the cost of log storage.

Databases Software Engineering

Tools and Benchmarks for Automated Log Parsing

8 code implementations8 Nov 2018 Jieming Zhu, Shilin He, Jinyang Liu, Pinjia He, Qi Xie, Zibin Zheng, Michael R. Lyu

Logs are imperative in the development and maintenance process of many software systems.

Software Engineering

A Directed Acyclic Graph Approach to Online Log Parsing

no code implementations12 Jun 2018 Pinjia He, Jieming Zhu, Pengcheng Xu, Zibin Zheng, Michael R. Lyu

A typical log-based system reliability management procedure is to first parse log messages because of their unstructured format; and apply data mining techniques on the parsed logs to obtain critical system behavior information.

Software Engineering

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