Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Zuoxi Yang

dblp:231/8543 · DBLP profile ↗
← Back
4ranked-venue papers
4as first author
2since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 2 first-author · 1 since 2021Databases, data management, data science and information retrieval · 2 · 2 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
2 papers
Knowledge graphs · 39% Information retrieval · 39% Recommender systems · 22%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Recommender systems › explainable recommendation
explanation generation
0.612022
Generating Knowledge-based Explanation for Recommendation from Review · SIGIR 2022
Information retrieval › document retrieval › domain-specific retrieval
biomedical information retrieval
0.412020
Biomedical Information Retrieval incorporating Knowledge Graph for Explainable Precision Medicine · SIGIR 2020
Information retrieval › document retrieval › domain-specific retrieval › biomedical information retrieval
precision medicine search
0.412020
Biomedical Information Retrieval incorporating Knowledge Graph for Explainable Precision Medicine · SIGIR 2020
Information retrieval › multimedia analysis and retrieval
semantic gap
0.112020
Biomedical Information Retrieval incorporating Knowledge Graph for Explainable Precision Medicine · SIGIR 2020

Methods — techniques the papers use, named apart from their topics

review mining · 0.6knowledge graph embedding · 0.4
YearPublicationVenuePosition
2022 Generating Knowledge-based Explanation for Recommendation from Review
abstract
Reasonable explanation is helpful to increase the trust and satisfaction of user to the recommender system. Among many previous studies, there is growing concern about generating explanation based on review text.
Zuoxi Yang
SIGIR1
2021 GFE: General Knowledge Enhanced Framework for Explainable Sequential Recommendation
Zuoxi Yang, Shoubin Dong, Jinlong Hu 0002
Knowl. Based Syst.1
2020 Biomedical Information Retrieval incorporating Knowledge Graph for Explainable Precision Medicine
abstract
As for many complex diseases, there is no "one size fits all" solutions for patients with a particular diagnosis in practice, which should be treated depends on patient's genetic, environmental, lifestyle choices and so on. Precision medicine can provide personalized treatment for a particular patient that has been drawn more and more attention. There are a large number of treatment options, which is overwhelming for clinicians to make best treatment for a particular patient. One of the effective ways to alleviate this problem is biomedical information retrieval system, which can automatically find out relevant information and proper treatment from mass of alternative treatments and cases. However, in the biomedical literature and clinical trials, there is a larger number of synonymous, polysemous and context terms, causing the semantic gap between query and document in traditional biomedical information retrieval systems.
Zuoxi Yang
SIGIR1
2020 HAGERec: Hierarchical Attention Graph Convolutional Network Incorporating Knowledge Graph for Explainable Recommendation
Zuoxi Yang, Shoubin Dong
Knowl. Based Syst.1