281 citations · 986 across the 37 of their papers we have counts for
37 papers
Semantic-Guided Feature Distillation for Multimodal Recommendation
Fan Liu, Huilin Chen, Zhiyong Cheng +2
Multimodal recommendation exploits the rich multimodal information associated with users or items to enhance the representation learning for better performance. In these methods, e…
Sample Less, Learn More: Efficient Action Recognition via Frame Feature Restoration
Harry Cheng, Yangyang Guo, Liqiang Nie +2
Training an effective video action recognition model poses significant computational challenges, particularly under limited resource budgets. Current methods primarily aim to eithe…
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
Qingyao Ai, Ting Bai, Zhao Cao +30
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Mod…
MB-HGCN: A Hierarchical Graph Convolutional Network for Multi-behavior Recommendation
Mingshi Yan, Zhiyong Cheng, Jing Sun +2
Collaborative filtering-based recommender systems that rely on a single type of behavior often encounter serious sparsity issues in real-world applications, leading to unsatisfacto…
Multi-Behavior Recommendation with Cascading Graph Convolution Networks
Zhiyong Cheng, Sai Han, Fan Liu +3
Multi-behavior recommendation, which exploits auxiliary behaviors (e.g., click and cart) to help predict users' potential interactions on the target behavior (e.g., buy), is regard…
Privacy-Preserving Synthetic Data Generation for Recommendation Systems
Fan Liu, Zhiyong Cheng, Huilin Chen +3
Recommendation systems make predictions chiefly based on users' historical interaction data (e.g., items previously clicked or purchased). There is a risk of privacy leakage when c…
Temporal Action Localization with Multi-temporal Scales
Zan Gao, Xinglei Cui, Tao Zhuo +4
Temporal action localization plays an important role in video analysis, which aims to localize and classify actions in untrimmed videos. The previous methods often predict actions…
A Unified End-to-End Retriever-Reader Framework for Knowledge-based VQA
Yangyang Guo, Liqiang Nie, Yongkang Wong +3
Knowledge-based Visual Question Answering (VQA) expects models to rely on external knowledge for robust answer prediction. Though significant it is, this paper discovers several le…
Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation
Mingshi Yan, Zhiyong Cheng, Chen Gao +4
Multi-behavior recommendation exploits multiple types of user-item interactions to alleviate the data sparsity problem faced by the traditional models that often utilize only one t…
Disentangled Multimodal Representation Learning for Recommendation
Fan Liu, Huilin Chen, Zhiyong Cheng +3
Many multimodal recommender systems have been proposed to exploit the rich side information associated with users or items (e.g., user reviews and item images) for learning better…
On Modality Bias Recognition and Reduction
Yangyang Guo, Liqiang Nie, Harry Cheng +3
Making each modality in multi-modal data contribute is of vital importance to learning a versatile multi-modal model. Existing methods, however, are often dominated by one or few o…
When Product Search Meets Collaborative Filtering: A Hierarchical Heterogeneous Graph Neural Network Approach
Xiangkun Yin, Yangyang Guo, Liqiang Nie +1
Personalization lies at the core of boosting the product search system performance. Prior studies mainly resorted to the semantic matching between textual queries and user/product…
Two-pronged Strategy: Lightweight Augmented Graph Network Hashing for Scalable Image Retrieval
Hui Cui, Lei Zhu, Jingjing Li +2
Hashing learns compact binary codes to store and retrieve massive data efficiently. Particularly, unsupervised deep hashing is supported by powerful deep neural networks and has th…
UGRec: Modeling Directed and Undirected Relations for Recommendation
Xinxiao Zhao, Zhiyong Cheng, Lei Zhu +2
Recommender systems, which merely leverage user-item interactions for user preference prediction (such as the collaborative filtering-based ones), often face dramatic performance d…
AdaVQA: Overcoming Language Priors with Adapted Margin Cosine Loss
Yangyang Guo, Liqiang Nie, Zhiyong Cheng +3
A number of studies point out that current Visual Question Answering (VQA) models are severely affected by the language prior problem, which refers to blindly making predictions ba…
Interest-aware Message-Passing GCN for Recommendation
Fan Liu, Zhiyong Cheng, Lei Zhu +2
Graph Convolution Networks (GCNs) manifest great potential in recommendation. This is attributed to their capability on learning good user and item embeddings by exploiting the col…
Feature-level Attentive ICF for Recommendation
Zhiyong Cheng, Fan Liu, Shenghan Mei +3
Item-based collaborative filtering (ICF) enjoys the advantages of high recommendation accuracy and ease in online penalization and thus is favored by the industrial recommender sys…
Loss re-scaling VQA: Revisiting the LanguagePrior Problem from a Class-imbalance View
Yangyang Guo, Liqiang Nie, Zhiyong Cheng +2
Recent studies have pointed out that many well-developed Visual Question Answering (VQA) models are heavily affected by the language prior problem, which refers to making predictio…
Frame-wise Cross-modal Matching for Video Moment Retrieval
Haoyu Tang, Jihua Zhu, Meng Liu +2
Video moment retrieval targets at retrieving a moment in a video for a given language query. The challenges of this task include 1) the requirement of localizing the relevant momen…
Dual-level Semantic Transfer Deep Hashing for Efficient Social Image Retrieval
Lei Zhu, Hui Cui, Zhiyong Cheng +2
Social network stores and disseminates a tremendous amount of user shared images. Deep hashing is an efficient indexing technique to support large-scale social image retrieval, due…
Enhancing Factorization Machines with Generalized Metric Learning
Yangyang Guo, Zhiyong Cheng, Jiazheng Jing +3
Factorization Machines (FMs) are effective in incorporating side information to overcome the cold-start and data sparsity problems in recommender systems. Traditional FMs adopt the…
Task-adaptive Asymmetric Deep Cross-modal Hashing
Fengling Li, Tong Wang, Lei Zhu +2
Supervised cross-modal hashing aims to embed the semantic correlations of heterogeneous modality data into the binary hash codes with discriminative semantic labels. Because of its…
Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation
Yang Xu, Lei Zhu, Zhiyong Cheng +2
Hashing is an effective technique to address the large-scale recommendation problem, due to its high computation and storage efficiency on calculating the user preferences on items…
A^2-GCN: An Attribute-aware Attentive GCN Model for Recommendation
Fan Liu, Zhiyong Cheng, Lei Zhu +2
As important side information, attributes have been widely exploited in the existing recommender system for better performance. In the real-world scenarios, it is common that some…