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- Xiaofang ZhouSuggested from co-authorship
- Dawei SongSuggested from co-authorship
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Works336 from public data
- Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation634
Visualization results of the learned representations validate that DuoRec can largely alleviate the representation degeneration problem, and a model-level augmentation is proposed based on Dropout to enable better semantic preserving.
- Inter-media hashing for large-scale retrieval from heterogeneous data sources620
A novel inter-media hashing (IMH) model is proposed to explore the correlations among multiple media types from different data sources and tackle the scalability issue, which transforms multimedia data from heterogeneous data sources into a common Hamming space, in which fast search can be easily implemented by XOR and bit-count operations.
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- Self-Supervised Learning for Recommender Systems: A Survey463
This survey paper presents a systematic and timely review of research efforts on self-supervised recommendation (SSR), and proposes an exclusive definition of SSR, on top of which a comprehensive taxonomy to divide existing SSR methods into four categories: contrastive, generative, predictive, and hybrid.
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- Learning to Diversify for Single Domain Generalization364
This paper proposes a style-complement module to enhance the generalization power of the model by synthesizing images from diverse distributions that are complementary to the source ones, and surpasses the state-of-the-art single-DG methods by up to 25.14%.
- Leveraging the Invariant Side of Generative Zero-Shot Learning347
A novel method is proposed which can directly generate the unseen features from random noises which are conditioned by the semantic descriptions, and is named leveraging invariant side GAN (LisGAN), which can outperform state-of-the-art methods with significant improvements.
- Multiple feature hashing for real-time large scale near-duplicate video retrieval333
This paper presents a novel approach - Multiple Feature Hashing (MFH) to tackle both the accuracy and the scalability issues of NDVR and shows that the proposed method outperforms the state-of-the-art techniques in both accuracy and efficiency.
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- Linear cross-modal hashing for efficient multimedia search299
This paper proposes a novel cross-modal hashing approach with a linear time complexity to the training data size, to enable scalable indexing for multimedia search across multiple modals and proves that this new representation preserves the intra-similarity in each modal.
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- GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection221
The proposed GraphRfi - a GCN-based user representation learning framework to perform robust recommendation and fraudster detection in a unified way is proposed, validated to be more robust to the various types of shilling attacks over the state-of-the-art recommender systems.
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- Enhancing Social Recommendation With Adversarial Graph Convolutional Networks190
A deep adversarial framework based on graph convolutional networks (GCN) is proposed to address the problems of social recommender systems and adopts adversarial training to unify all the components by playing a Minimax game and ensure a coordinated effort to enhance recommendation performance.
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- Learning Private Neural Language Modeling with Attentive Aggregation180
This work proposes a novel model aggregation with an attention mechanism considering the contribution of client models to the global model, together with an optimization technique during server aggregation that minimizes the weighted distance between the server model and client models.
- Near-duplicate video retrieval180
As discovered in recent works, latest improvements and progress in near-duplicate video retrieval, as well as related topics including low-level feature extraction, signature generation, and high-dimensional indexing, are employed to assist the process.
- Joint Modeling of User Check-in Behaviors for Real-time Point-of-Interest Recommendation171
A unified probabilistic generative model, the Topic-Region Model (TRM), to simultaneously discover the semantic, temporal, and spatial patterns of users’ check-in activities, and to model their joint effect on Users’ decision making for selection of POIs to visit is proposed.
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- From Zero-Shot Learning to Cold-Start Recommendation165
This work formulates CSR as a ZSL problem, and a tailor-made ZSL method is proposed to handle CSR, which challenges three cruxes, i.e., domain shift, spurious correlations and computing efficiency, in this paper.
- Semantics Disentangling for Generalized Zero-Shot Learning159
A novel semantics disentangling framework is proposed for the generalized zero-shot learning task (SDGZSL), where the visual features of unseen classes are firstly estimated by a conditional VAE and then factorized into semantic-consistent and semantic-unrelated latent vectors.
- Neural Memory Streaming Recommender Networks with Adversarial Training159
An adaptive negative sampling framework based on Generative Adversarial Nets (GAN) is developed to optimize the proposed streaming recommender model, which effectively overcomes the limitations of classical negative sampling approaches and improves both effectiveness and efficiency of the model parameter inference.
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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