Search Results for author: Qiannan Zhu

Found 7 papers, 5 papers with code

Cognitive Personalized Search Integrating Large Language Models with an Efficient Memory Mechanism

no code implementations16 Feb 2024 Yujia Zhou, Qiannan Zhu, Jiajie Jin, Zhicheng Dou

To counter this limitation, personalized search has been developed to re-rank results based on user preferences derived from query logs.

CMMU: A Benchmark for Chinese Multi-modal Multi-type Question Understanding and Reasoning

1 code implementation25 Jan 2024 Zheqi He, Xinya Wu, Pengfei Zhou, Richeng Xuan, Guang Liu, Xi Yang, Qiannan Zhu, Hua Huang

However, the mastery of domain-specific knowledge, which is essential for evaluating the intelligence of MLLMs, continues to be a challenge.

Multiple-choice Position

Learning Explicit User Interest Boundary for Recommendation

1 code implementation22 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 +1

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 Relation +1

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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