Search Results for author:
Found 7 papers, 4 papers with code
Dan: Deep attention neural network for news recommendation
With the rapid information explosion of news, making personalized news recommendation for users becomes an increasingly challenging problem.
A Relation-Specific Attention Network for Joint Entity and Relation Extraction
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.
Ranked #11 on
Relation Extraction
on WebNLG
How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View
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.
Ranked #12 on
Link Prediction
on FB15k-237
Is There More Pattern in Knowledge Graph? Exploring Proximity Pattern for Knowledge Graph Embedding
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.
Learning Explicit User Interest Boundary for Recommendation
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.
CMMU: A Benchmark for Chinese Multi-modal Multi-type Question Understanding and Reasoning
Current multi-modal benchmarks for domain-specific knowledge concentrate on multiple-choice questions and are predominantly available in English, which imposes limitations on the comprehensiveness of the evaluation.
Cognitive Personalized Search Integrating Large Language Models with an Efficient Memory Mechanism
To counter this limitation, personalized search has been developed to re-rank results based on user preferences derived from query logs.
