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20172023
most citedGAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction

96 citations · 557 across the 42 of their papers we have counts for

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Showing cs.IRShow all

6 papers · 1 filter

cs.IR20239 cited

HiPrompt: Few-Shot Biomedical Knowledge Fusion via Hierarchy-Oriented Prompting

Jiaying Lu, Jiaming Shen, Bo Xiong +3

Medical decision-making processes can be enhanced by comprehensive biomedical knowledge bases, which require fusing knowledge graphs constructed from different sources via a unifor…

cs.IR20222 cited

Partial Relaxed Optimal Transport for Denoised Recommendation

Yanchao Tan, Carl Yang Member, Xiangyu Wei +2

The interaction data used by recommender systems (RSs) inevitably include noises resulting from mistaken or exploratory clicks, especially under implicit feedbacks. Without proper…

cs.IR2022

How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generation

Hejie Cui, Jiaying Lu, Yao Ge +1

Graph neural networks (GNNs), as a group of powerful tools for representation learning on irregular data, have manifested superiority in various downstream tasks. With unstructured…

cs.IR20211 cited

Controllable Gradient Item Retrieval

Haonan Wang, Chang Zhou, Carl Yang +2

In this paper, we identify and study an important problem of gradient item retrieval. We define the problem as retrieving a sequence of items with a gradual change on a certain att…

cs.IR20211 cited

Multi-Facet Recommender Networks with Spherical Optimization

Yanchao Tan, Carl Yang, Xiangyu Wei +2

Implicit feedback is widely explored by modern recommender systems. Since the feedback is often sparse and imbalanced, it poses great challenges to the learning of complex interact…

cs.IR2019

Query-Specific Knowledge Summarization with Entity Evolutionary Networks

Carl Yang, Lingrui Gan, Zongyi Wang +3

Given a query, unlike traditional IR that finds relevant documents or entities, in this work, we focus on retrieving both entities and their connections for insightful knowledge su…