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- 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.
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- 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.
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- 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.
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- 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.
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- 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.
- A Hybrid Framework for Text Modeling with Convolutional RNN69
A generic inference hybrid framework for Convolutional Recurrent Neural Network (conv-RNN) of semantic modeling of text is introduced, seamless integrating the merits on extracting different aspects of linguistic information from both convolutional and recurrent neural network structures and thus strengthening the semantic understanding power of the new framework.
- BaGuaLu65
BaGuaLu is proposed, the first work targeting training brain scale models on an entire exascale supercomputer, the New Generation Sunway Supercomputer, by combining hardware-specific intra-node optimization and hybrid parallel strategies, which enables decent performance and scalability on unprecedentedly large models.
- Revisiting Multimodal Representation in Contrastive Learning: From Patch and Token Embeddings to Finite Discrete Tokens63
It is demonstrated that using FDT representations in CLIP-style models improves cross-modal alignment and performance in visual recognition and vision-language downstream tasks, and it is shown that the method can learn more comprehensive representations, and the learned FDT capture meaningful cross- modal correspondence, ranging from objects to actions and attributes.
- A Practical Framework of Conversion Rate Prediction for Online Display Advertising58
This paper proposes a safe prediction framework with conversion attribution adjustment to handle over-predictions and to further alleviate over-bidding at different levels and illustrates both offline and online experimental results to demonstrate the effectiveness of the framework.
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- STAM: A Spatiotemporal Aggregation Method for Graph Neural Network-based Recommendation54
A spatiotemporal aggregation method STAM is proposed to efficiently incorporate temporal information into neighbor embedding learning and brings significant improvements on GNN-based recommendation compared with spatial-based aggregation methods.
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- BiANE52
This paper proposes a novel model called BiANE, short for bipartite Attributed Network Embedding, which not only models the inter-partition proximity but also models the intra-partitions proximity through a novel latent correlation training approach.
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- Prevalence of psychological disorders in the COVID-19 epidemic in China: A real world cross-sectional study49
F Females, non-medical workers and those with a history of chronic diseases have had higher risks for depression, insomnia, and anxiety, and positive COVID-19 infection status was associated with higher risk of depression, insomnia, and anxiety in medical workers.
- Will Triadic Closure Strengthen Ties in Social Networks?49
A TRIST method is proposed—a kernel density estimation (KDE)-based graphical model—to solve the triadic tie strength dynamics prediction problem by incorporating user demographics, temporal effects, and structural information and it is demonstrated that TRIST offers a greater than 82% potential predictability for inferring triadic Tie strength dynamics in both networks.
- Hierarchical Representation Learning for Bipartite Graphs47
This work proposes to cluster the users into a set of communities and make recommendations based on the information of the users in the community collectively, which can be considered as an enhancement to the GNN methods that are inherently flat and do not learn hierarchical representations of graphs.
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- OAG-BERT: Towards a Unified Backbone Language Model for Academic Knowledge Services44
This work pre-train an academic language model, namely OAG-BERT, to integrate massive heterogeneous entity knowledge beyond scientific corpora to create a unified backbone language model for various knowledge-intensive academic knowledge mining challenges.
- Interactive Paths Embedding for Semantic Proximity Search on Heterogeneous Graphs41
This paper introduces a novel concept of interactive paths to model the inter-dependency among multiple paths between a query object and a target object, and proposes an Interactive Paths Embedding (IPE) model, which learns low-dimensional representations for the resulting interactive-paths structures for proximity estimation.
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- Heterogeneous Embedding Propagation for Large-Scale E-Commerce User Alignment38
A novel Heterogeneous Embedding Propagation model is proposed, which is to iteratively reconstruct a node's embedding from its heterogeneous neighbors in a weighted manner, and meanwhile propagate its embedding updates from reconstruction loss and/or classification loss to its neighbors.
- Connecting Language and Vision for Natural Language-Based Vehicle Retrieval37
This work proposes to jointly train the state-of-the-art vision models with the transformer-based language model in an end-to-end manner to connect language and vision, and achieves the 1st place on the 5th AI City Challenge.
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- Bayes EMbedding (BEM)30
BEM is presented, a Bayesian framework that incorporates the information from knowledge graphs and behavior graphs and integrates them with the pre-trained embeddings from the behavior graphs via aBayesian generative model and is able to mutually refine the embeddeds from both sides while preserving their own topological structures.
- Subgraph-augmented Path Embedding for Semantic User Search on Heterogeneous Social Network29
A new concept of subgraph-augmented path for semantic user search is introduced, which considers sampling a set of object paths from a query user to a target user, and replaces the linear object sequence between its every two neighboring users with their shared subgraph instances.
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- Retrieval-Augmented Mixture of LoRA Experts for Uploadable Machine Learning18
Retrieval-Augmented Mixture of LoRA Experts (RAMoLE), a framework that adaptively retrieves and composes multiple LoRAs based on input prompts, is proposed, highlighting its effectiveness and scalability.
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- Causal Distillation for Alleviating Performance Heterogeneity in Recommender Systems18
Through in-depth analysis, it is found that CausalD largely improves the performance of tail users, reduces the performance heterogeneity, and enhances the overall performance.
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- SLED: Structure Learning based Denoising for Recommendation16
It is identified that an interaction’s related structural patterns in its neighborhood graph are potentially correlated with some outcome of implicit feedback (i.e., users’ ratings after consuming items), analogous to findings in other domains such as social networks.
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- Learning with Group Noise11
This work proposes a novel Max-Matching method for learning with group noise that utilizes a matching mechanism to evaluate the relation confidence of each object w.r.t. the target, which considers the Non-IID characteristics among objects in the group.
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- DavIR: Data Selection via Implicit Reward for Large Language Models9
DavIR generalizes Reducible Holdout Loss to core-set selection problem of causal language modeling, and quantifies the learnability of a given datum with respect to a pre-trained LLM based on relative reduction in loss during fine-tuning, a metric it is shown to be closely related to the implicit reward model described in Direct Preference Optimization.
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- Uncovering Causal Effects of Online Short Videos on Consumer Behaviors9
Both causal effects (i.e., informative and persuasive effects) are significant and have a strong correlation with the short videos' subjectivity scores, and the signs of these correlations vary with the prices of the products.
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- Research on Nonlinear Model for Friction Pile Top Axial Load and Settlement of Soft Soil Foundations–
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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.
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