Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks243 from public data
- GraphMAE: Self-Supervised Masked Graph Autoencoders1,076
This study identifies and examines the issues that negatively impact the development of GAEs, including their reconstruction objective, training robustness, and error metric, and presents a masked graph autoencoder GraphMAE that mitigates these issues for generative self-supervised graph learning.
- Representation Learning for Attributed Multiplex Heterogeneous Network498
Results of the offline A/B tests on product recommendation further confirm the effectiveness and efficiency of the framework in practice, and the theoretical analysis of the proposed framework gives its connection with previous works and proving its better expressiveness.
- AliGraph478
This paper presents a comprehensive graph neural network system, namely AliGraph, which consists of distributed graph storage, optimized sampling operators and runtime to efficiently support not only existing popular GNNs but also a series of in-house developed ones for different scenarios.
- Controllable Multi-Interest Framework for Recommendation422
This paper proposes a novel controllable multi-interest framework for the sequential recommendation, called ComiRec, which captures multiple interests from user behavior sequences, which can be exploited for retrieving candidate items from the large-scale item pool.
- Towards Knowledge-Based Recommender Dialog System318
A novel end-to-end framework called KBRD, which stands for Knowledge-Based Recommender Dialog System, integrates the recommender system and the dialog generation system and shows that the two systems can bring mutual benefits to each other.
- Disentangled Self-Supervision in Sequential Recommenders271
This paper proposes a sequence-to-sequence (seq2seq) training strategy based on latent self-supervision and disentanglement, which performs self- supervision in the latent space, instead of reconstructing the items in the future sequence individually.
- 250
- Cognitive Graph for Multi-Hop Reading Comprehension at Scale249
The implementation based on BERT and graph neural network efficiently handles millions of documents for multi-hop reasoning questions in the HotpotQA fullwiki dataset, achieving a winning joint F_1 score of 34.9 on the leaderboard.
- Understanding Negative Sampling in Graph Representation Learning234
This work systematically analyzes the role of negative sampling from the perspectives of both objective and risk, theoretically demonstrating that negative sampling is as important as positive sampling in determining the optimization objective and the resulted variance.
- ANRL: Attributed Network Representation Learning via Deep Neural Networks234
This paper proposes a novel framework, named ANRL, to incorporate both the network structure and node attribute information in a principled way, and proposes a neighbor enhancement autoencoder to model the nodes attribute information, which reconstructs its target neighbors instead of itself.
- Sparse-Interest Network for Sequential Recommendation192
A novel Sparse Interest NEtwork (SINE) is proposed, which develops an interest aggregation module to actively predict the user's current intention and then use it to explicitly model multiple interests for next-item prediction.
- Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI186
This survey is the first to set up the collaborative learning mechanism for cloud and edge modeling with a thorough review of the architectures that enable such mechanism, and discusses potentials and practical experiences of some on-going advanced edge AI topics including pretraining models, graph neural networks and reinforcement learning.
- Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems175
CLRec is designed, a contrastive learning method to improve DCG in terms of fairness, effectiveness and efficiency in recommender systems with extremely large candidate size, and improved upon CLRec and proposes Multi-CLRec, for accurate multi-intention aware bias reduction.
- CogLTX: Applying BERT to Long Texts163
The proposed CogLTX 1 framework identifies key sentences by training a judge model, concatenates them for reasoning, and enables multi-step reasoning via rehearsal and decay, and outperforms or gets comparable results to SOTA models on various downstream tasks with memory overheads independent of the length of text.
- 147
- Sequential Scenario-Specific Meta Learner for Online Recommendation131
This paper combines the scenario-specific learning with a model-agnostic sequential meta-learning and unify them into an integrated end-to-end framework, namely S cenario-specific S equential Meta learner (or s^2Meta).
- Learning to Hash with Graph Neural Networks for Recommender Systems108
This work investigates the problem of hashing with graph neural networks (GNNs) for high quality retrieval, and proposes a simple yet effective discrete representation learning framework to jointly learn continuous and discrete codes.
- Is a Single Vector Enough?93
This paper proposes a polysemous embedding approach for modeling multiple facets of nodes, as motivated by the phenomenon of word polysemy in language modeling, which is adaptive to various existing embedding models, without significantly complicating the optimization process.
- 87
- Dynamic Memory based Attention Network for Sequential Recommendation79
A novel long sequential recommendation model, called Dynamic Memory-based Attention Network (DMAN), which segments the overall long behavior sequence into a series of sub-sequences, then trains the model and maintains a set of memory blocks to preserve long-term interests of users.
- Device-Cloud Collaborative Learning for Recommendation78
This work is among the first attempts to study the Device-Cloud Collaborative Learning (DCCL) framework and proposes a novel MetaPatch learning approach on the device side to efficiently achieve "thousands of people with thousands of models'' given a centralized cloud model.
- 77
- DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization72
It is argued that the computational cost brought by fine-tuning can be rather unnecessary, and a novel perspective to improving DMG without increasing computational cost is presented, i.e., device-specific parameter generation which directly maps data distribution to parameters.
- Dependent Hierarchical Beta Process for Image Interpolation and Denoising72
A dependent hierarchical beta process is developed as a prior for data that may be represented in terms of a sparse set of latent features, with covariate-dependent feature usage, applicable to general covariates and data models.
- Adversarial Detection with Model Interpretation69
A novel adversary-resistant detection framework is developed by utilizing the interpretation of ML models, which explains the mechanism of how the target ML model makes prediction for a given instance, thus providing more insights for crafting adversarial samples.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.