Search Results for author: Qiannan Zhu

Found 5 papers, 3 papers with code

Learning Explicit User Interest Boundary for Recommendation

no code implementations22 Nov 2021 Jianhuan Zhuo, Qiannan Zhu, Yinliang Yue, Yuhong Zhao

The core objective of modelling recommender systems from implicit feedback is to maximize the positive sample score $s_p$ and minimize the negative sample score $s_n$, which can usually be summarized into two paradigms: the pointwise and the pairwise.

Recommendation Systems

Is There More Pattern in Knowledge Graph? Exploring Proximity Pattern for Knowledge Graph Embedding

no code implementations2 Oct 2021 Ren Li, Yanan Cao, Qiannan Zhu, Xiaoxue Li, Fang Fang

Modeling of relation pattern is the core focus of previous Knowledge Graph Embedding works, which represents how one entity is related to another semantically by some explicit relation.

Knowledge Graph Completion Knowledge Graph Embedding

How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View

1 code implementation24 Sep 2021 Ren Li, Yanan Cao, Qiannan Zhu, Guanqun Bi, Fang Fang, Yi Liu, Qian Li

However, most existing KGE works focus on the design of delicate triple modeling function, which mainly tells us how to measure the plausibility of observed triples, but offers limited explanation of why the methods can extrapolate to unseen data, and what are the important factors to help KGE extrapolate.

Knowledge Graph Completion Knowledge Graph Embedding +1

A Relation-Specific Attention Network for Joint Entity and Relation Extraction

1 code implementation1 Jul 2020 Yue Yuan, Xiaofei Zhou, Shirui Pan, Qiannan Zhu, Zeliang Song, Li Guo

Joint extraction of entities and relations is an important task in natural language processing (NLP), which aims to capture all relational triplets from plain texts.

Joint Entity and Relation Extraction

Dan: Deep attention neural network for news recommendation

1 code implementation AAAI 2019 Qiannan Zhu

With the rapid information explosion of news, making personalized news recommendation for users becomes an increasingly challenging problem.

Deep Attention News Recommendation

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