Search Results for author: Fajie Yuan

Found 28 papers, 16 papers with code

Hypergraph Node Representation Learning with One-Stage Message Passing

no code implementations1 Dec 2023 Shilin Qu, Weiqing Wang, Yuan-Fang Li, Xin Zhou, Fajie Yuan

HGraphormer injects the hypergraph structure information (local information) into Transformers (global information) by combining the attention matrix and hypergraph Laplacian.

Representation Learning

FMMRec: Fairness-aware Multimodal Recommendation

no code implementations26 Oct 2023 Weixin Chen, Li Chen, Yongxin Ni, Yuhan Zhao, Fajie Yuan, Yongfeng Zhang

Recently, multimodal recommendations have gained increasing attention for effectively addressing the data sparsity problem by incorporating modality-based representations.

counterfactual Fairness +2

A Content-Driven Micro-Video Recommendation Dataset at Scale

1 code implementation27 Sep 2023 Yongxin Ni, Yu Cheng, Xiangyan Liu, Junchen Fu, Youhua Li, Xiangnan He, Yongfeng Zhang, Fajie Yuan

Micro-videos have recently gained immense popularity, sparking critical research in micro-video recommendation with significant implications for the entertainment, advertising, and e-commerce industries.

Benchmarking Recommendation Systems +1

An Image Dataset for Benchmarking Recommender Systems with Raw Pixels

2 code implementations13 Sep 2023 Yu Cheng, Yunzhu Pan, JiaQi Zhang, Yongxin Ni, Aixin Sun, Fajie Yuan

Then, to show the effectiveness of the dataset's image features, we substitute the itemID embeddings (from IDNet) with a powerful vision encoder that represents items using their raw image pixels.

Benchmarking Recommendation Systems

Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights

no code implementations19 May 2023 Ruyu Li, Wenhao Deng, Yu Cheng, Zheng Yuan, JiaQi Zhang, Fajie Yuan

Furthermore, we compare the performance of the TCF paradigm utilizing the most powerful LMs to the currently dominant ID embedding-based paradigm and investigate the transferability of this TCF paradigm.

Collaborative Filtering News Recommendation +1

Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited

1 code implementation24 Mar 2023 Zheng Yuan, Fajie Yuan, Yu Song, Youhua Li, Junchen Fu, Fei Yang, Yunzhu Pan, Yongxin Ni

In fact, this question was answered ten years ago when IDRec beats MoRec by a strong margin in both recommendation accuracy and efficiency.

Recommendation Systems

Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems

1 code implementation13 Oct 2022 Guanghu Yuan, Fajie Yuan, Yudong Li, Beibei Kong, Shujie Li, Lei Chen, Min Yang, Chenyun Yu, Bo Hu, Zang Li, Yu Xu, XiaoHu Qie

Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback.

Recommendation Systems

Exploring evolution-aware & -free protein language models as protein function predictors

1 code implementation14 Jun 2022 Mingyang Hu, Fajie Yuan, Kevin K. Yang, Fusong Ju, Jin Su, Hui Wang, Fei Yang, Qiuyang Ding

Large-scale Protein Language Models (PLMs) have improved performance in protein prediction tasks, ranging from 3D structure prediction to various function predictions.

Multiple Sequence Alignment

TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback

no code implementations13 Jun 2022 Jie Wang, Fajie Yuan, Mingyue Cheng, Joemon M. Jose, Chenyun Yu, Beibei Kong, Xiangnan He, Zhijin Wang, Bo Hu, Zang Li

That is, the users and the interacted items are represented by their unique IDs, which are generally not shareable across different systems or platforms.

Recommendation Systems Transfer Learning

Enhancing Top-N Item Recommendations by Peer Collaboration

no code implementations31 Oct 2021 Yang Sun, Fajie Yuan, Min Yang, Alexandros Karatzoglou, Shen Li, Xiaoyan Zhao

In this paper, we plan to exploit such redundancy phenomena to improve the performance of RS.

Recommendation Systems

Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking

1 code implementation ACL 2021 Binzong Geng, Fajie Yuan, Qiancheng Xu, Ying Shen, Ruifeng Xu, Min Yang

This ability to learn consecutive tasks without forgetting how to perform previously trained problems is essential for developing an online dialogue system.

Continual Learning Network Pruning

Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Search

no code implementations15 Jul 2021 Lei Chen, Fajie Yuan, Jiaxi Yang, Min Yang, Chengming Li

To realize such a goal, we propose AdaRec, a knowledge distillation (KD) framework which compresses knowledge of a teacher model into a student model adaptively according to its recommendation scene by using differentiable Neural Architecture Search (NAS).

Knowledge Distillation Neural Architecture Search +1

Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification

1 code implementation21 Jun 2021 Binzong Geng, Min Yang, Fajie Yuan, Shupeng Wang, Xiang Ao, Ruifeng Xu

In this paper, we propose a novel iterative network pruning with uncertainty regularization method for lifelong sentiment classification (IPRLS), which leverages the principles of network pruning and weight regularization.

Network Pruning Sentiment Analysis +1

User-specific Adaptive Fine-tuning for Cross-domain Recommendations

no code implementations15 Jun 2021 Lei Chen, Fajie Yuan, Jiaxi Yang, Xiangnan He, Chengming Li, Min Yang

Fine-tuning works as an effective transfer learning technique for this objective, which adapts the parameters of a pre-trained model from the source domain to the target domain.

Recommendation Systems Transfer Learning

StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking

1 code implementation14 Dec 2020 Jiachun Wang, Fajie Yuan, Jian Chen, Qingyao Wu, Min Yang, Yang Sun, Guoxiao Zhang

We validate the performance of StackRec by instantiating it with four state-of-the-art SR models in three practical scenarios with real-world datasets.

Sequential Recommendation

One Person, One Model, One World: Learning Continual User Representation without Forgetting

2 code implementations29 Sep 2020 Fajie Yuan, Guoxiao Zhang, Alexandros Karatzoglou, Joemon Jose, Beibei Kong, Yudong Li

In this paper, we delve on research to continually learn user representations task by task, whereby new tasks are learned while using partial parameters from old ones.

Recommendation Systems

CmnRec: Sequential Recommendations with Chunk-accelerated Memory Network

1 code implementation28 Apr 2020 Shilin Qu, Fajie Yuan, Guibing Guo, Liguang Zhang, Wei Wei

Specifically, our framework divides proximal information units into chunks, and performs memory access at certain time steps, whereby the number of memory operations can be greatly reduced.

Chunking Recommendation Systems

A Generic Network Compression Framework for Sequential Recommender Systems

1 code implementation21 Apr 2020 Yang Sun, Fajie Yuan, Min Yang, Guoao Wei, Zhou Zhao, Duo Liu

Current state-of-the-art sequential recommender models are typically based on a sandwich-structured deep neural network, where one or more middle (hidden) layers are placed between the input embedding layer and output softmax layer.

Sequential Recommendation

Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation

1 code implementation13 Jan 2020 Fajie Yuan, Xiangnan He, Alexandros Karatzoglou, Liguang Zhang

To overcome this issue, we develop a parameter efficient transfer learning architecture, termed as PeterRec, which can be configured on-the-fly to various downstream tasks.

Recommendation Systems Transfer Learning

Modeling Embedding Dimension Correlations via Convolutional Neural Collaborative Filtering

1 code implementation26 Jun 2019 Xiaoyu Du, Xiangnan He, Fajie Yuan, Jinhui Tang, Zhiguang Qin, Tat-Seng Chua

In this work, we emphasize on modeling the correlations among embedding dimensions in neural networks to pursue higher effectiveness for CF.

Collaborative Filtering Recommendation Systems

Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation

no code implementations11 Jun 2019 Fajie Yuan, Xiangnan He, Haochuan Jiang, Guibing Guo, Jian Xiong, Zhezhao Xu, Yilin Xiong

To capture the sequential dependencies, existing methods resort either to data augmentation techniques or left-to-right style autoregressive training. Since these methods are aimed to model the sequential nature of user behaviors, they ignore the future data of a target interaction when constructing the prediction model for it.

Data Augmentation Sequential Recommendation +1

Adversarial Training Towards Robust Multimedia Recommender System

1 code implementation19 Sep 2018 Jinhui Tang, Xiaoyu Du, Xiangnan He, Fajie Yuan, Qi Tian, Tat-Seng Chua

To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning.

Information Retrieval Multimedia

A Simple Convolutional Generative Network for Next Item Recommendation

3 code implementations15 Aug 2018 Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose, Xiangnan He

Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation.

Recommendation Systems

VSE-ens: Visual-Semantic Embeddings with Efficient Negative Sampling

no code implementations5 Jan 2018 Guibing Guo, Songlin Zhai, Fajie Yuan, Yu-An Liu, Xingwei Wang

Jointing visual-semantic embeddings (VSE) have become a research hotpot for the task of image annotation, which suffers from the issue of semantic gap, i. e., the gap between images' visual features (low-level) and labels' semantic features (high-level).

Improving Negative Sampling for Word Representation using Self-embedded Features

no code implementations26 Oct 2017 Long Chen, Fajie Yuan, Joemon M. Jose, Wei-Nan Zhang

Although the word-popularity based negative sampler has shown superb performance in the skip-gram model, the theoretical motivation behind oversampling popular (non-observed) words as negative samples is still not well understood.

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