41 citations · 205 across the 22 of their papers we have counts for
22 papers
Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited
Zheng Yuan, Fajie Yuan, Yu Song +5
Recommendation models that utilize unique identities (IDs) to represent distinct users and items have been state-of-the-art (SOTA) and dominated the recommender systems (RS) litera…
Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights
Junchen Fu, Fajie Yuan, Yu Song +6
Adapters, a plug-in neural network module with some tunable parameters, have emerged as a parameter-efficient transfer learning technique for adapting pre-trained models to downstr…
Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights
Ruyu Li, Wenhao Deng, Yu Cheng +3
Text-based collaborative filtering (TCF) has emerged as the prominent technique for text and news recommendation, employing language models (LMs) as text encoders to represent item…
Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems
Guanghu Yuan, Fajie Yuan, Yudong Li +9
Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback. RS models evaluated on such datasets of…
Exploring evolution-aware & -free protein language models as protein function predictors
Mingyang Hu, Fajie Yuan, Kevin K. Yang +5
Large-scale Protein Language Models (PLMs) have improved performance in protein prediction tasks, ranging from 3D structure prediction to various function predictions. In particula…
TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback
Jie Wang, Fajie Yuan, Mingyue Cheng +6
Learning large-scale pre-trained models on broad-ranging data and then transfer to a wide range of target tasks has become the de facto paradigm in many machine learning (ML) commu…
Enhancing Top-N Item Recommendations by Peer Collaboration
Yang Sun, Fajie Yuan, Min Yang +3
Deep neural networks (DNN) have achieved great success in the recommender systems (RS) domain. However, to achieve remarkable performance, DNN-based recommender models often requir…
Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking
Binzong Geng, Fajie Yuan, Qiancheng Xu +3
This ability to learn consecutive tasks without forgetting how to perform previously trained problems is essential for developing an online dialogue system. This paper proposes an…
Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Search
Lei Chen, Fajie Yuan, Jiaxi Yang +2
Sequential recommender systems (SRS) have become a research hotspot due to its power in modeling user dynamic interests and sequential behavioral patterns. To maximize model expres…
Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification
Binzong Geng, Min Yang, Fajie Yuan +3
Lifelong learning capabilities are crucial for sentiment classifiers to process continuous streams of opinioned information on the Web. However, performing lifelong learning is non…
User-specific Adaptive Fine-tuning for Cross-domain Recommendations
Lei Chen, Fajie Yuan, Jiaxi Yang +3
Making accurate recommendations for cold-start users has been a longstanding and critical challenge for recommender systems (RS). Cross-domain recommendations (CDR) offer a solutio…
StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking
Jiachun Wang, Fajie Yuan, Jian Chen +4
Deep learning has brought great progress for the sequential recommendation (SR) tasks. With advanced network architectures, sequential recommender models can be stacked with many h…
One Person, One Model, One World: Learning Continual User Representation without Forgetting
Fajie Yuan, Guoxiao Zhang, Alexandros Karatzoglou +3
Learning user representations is a vital technique toward effective user modeling and personalized recommender systems. Existing approaches often derive an individual set of model…
A Generic Network Compression Framework for Sequential Recommender Systems
Yang Sun, Fajie Yuan, Min Yang +3
Sequential recommender systems (SRS) have become the key technology in capturing user's dynamic interests and generating high-quality recommendations. Current state-of-the-art sequ…
CmnRec: Sequential Recommendations with Chunk-accelerated Memory Network
Shilin Qu, Fajie Yuan, Guibing Guo +2
Recently, Memory-based Neural Recommenders (MNR) have demonstrated superior predictive accuracy in the task of sequential recommendations, particularly for modeling long-term item…
Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation
Fajie Yuan, Xiangnan He, Alexandros Karatzoglou +1
Inductive transfer learning has had a big impact on computer vision and NLP domains but has not been used in the area of recommender systems. Even though there has been a large bod…
Modeling Embedding Dimension Correlations via Convolutional Neural Collaborative Filtering
Xiaoyu Du, Xiangnan He, Fajie Yuan +3
As the core of recommender system, collaborative filtering (CF) models the affinity between a user and an item from historical user-item interactions, such as clicks, purchases, an…
Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation
Fajie Yuan, Xiangnan He, Haochuan Jiang +4
Session-based recommender systems have attracted much attention recently. To capture the sequential dependencies, existing methods resort either to data augmentation techniques or…
Adversarial Training Towards Robust Multimedia Recommender System
Jinhui Tang, Xiaoyu Du, Xiangnan He +3
With the prevalence of multimedia content on the Web, developing recommender solutions that can effectively leverage the rich signal in multimedia data is in urgent need. Owing to…
A Simple Convolutional Generative Network for Next Item Recommendation
Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis +2
Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation. An ordered collection of past items the user has interac…
VSE-ens: Visual-Semantic Embeddings with Efficient Negative Sampling
Guibing Guo, Songlin Zhai, Fajie Yuan +2
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…
Improving Negative Sampling for Word Representation using Self-embedded Features
Long Chen, Fajie Yuan, Joemon M. Jose +1
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…