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Works538 from public data
- NUS-WIDE3,409
The benchmark results indicate that it is possible to learn effective models from sufficiently large image dataset to facilitate general image retrieval and four research issues on web image annotation and retrieval are identified.
- MMGCN951
A Multi-modal Graph Convolution Network (MMGCN) framework built upon the message-passing idea of graph neural networks, which can yield modal-specific representations of users and micro-videos to better capture user preferences is designed.
- Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach676
This paper revisits GCN based CF models from two aspects and proposes a residual network structure that is specifically designed for CF with user-item interaction modeling, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse user- item interaction data.
- A Neural Influence Diffusion Model for Social Recommendation589
A deep influence propagation model is proposed to stimulate how users are influenced by the recursive social diffusion process for social recommendation, with more than 13% performance improvements over the best baselines for top-10 recommendation on the two datasets.
- Crowded Scene Analysis: A Survey488
The background knowledge and the available features related to crowded scenes are provided and existing models, popular algorithms, evaluation protocols, and system performance are provided corresponding to different aspects of the crowded scene analysis.
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- Deep Representation Learning With Part Loss for Person Re-Identification439
Experimental results on three person ReID datasets, i.e., Market1501, CUHK03, and VIPeR, show that the proposed deep representation learning procedure named part loss network outperforms existing deep representations.
- DiffNet++: A Neural Influence and Interest Diffusion Network for Social Recommendation372
DiffNet++ is proposed, an improved algorithm of DiffNet that models the neural influence diffusion and interest diffusion in a unified framework and reformulating the social recommendation as a heterogeneous graph with social network and interest network as input.
- Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems353
This work proposes a new CRS framework named Estimation"Action" Reflection, or EAR, which consists of three stages to better converse with users, and conducts extensive experiments on two datasets from Yelp and LastFM to demonstrate significant improvements over the state-of-the-art method CRM.
- Deep Item-based Collaborative Filtering for Top-N Recommendation339
This article proposes a more expressive ICF solution by accounting for the nonlinear and higher-order relationships among items, and treats this solution as a deep variant of ICF, thus term it as DeepICF.
- 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.
- 305
- Attentive Group Recommendation301
The AGREE model not only improves the group recommendation performance but also enhances the recommendation for users, especially for cold-start users that have no historical interactions individually.
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- Self-Supervised Video Hashing With Hierarchical Binary Auto-Encoder276
This paper proposes a novel unsupervised video hashing framework dubbed SSVH, which is able to capture the temporal nature of videos in an end-to-end learning to hash fashion, and designs a hierarchical binary auto-encoder to model the temporal dependencies in videos with multiple granularities.
- Image annotation by k NN-sparse graph-based label propagation over noisily tagged web images269
This article exploits the problem of annotating a large-scale image corpus by label propagation over noisily tagged web images by proposing a novel kNN-sparse graph-based semi-supervised learning approach for harnessing the labeled and unlabeled data simultaneously.
- Point-of-Interest Recommendations260
This work proposes to learn a set of potential locations that each individual's friends have checked-in before and this individual is most interested in to improve POI recommendation accuracy and address cold-start problem.
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- Learning to Compose and Reason with Language Tree Structures for Visual Grounding220
A natural language grounding model that can automatically compose a binary tree structure for parsing the language and then perform visual reasoning along the tree in a bottom-up fashion and achieves the state-of-the-art performance with more explainable reasoning is proposed.
- 217
- Inferring semantic concepts from community-contributed images and noisy tags212
This paper proposes a novel sparse graph-based semi-supervised learning approach for harnessing the labeled and unlabeled data simultaneously, and constructs an informative compact concept space with small semantic gap to infer the semantic concepts in this space to bridge the semantic gap.
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 2026-10-14. 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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