Demo Data from VLDB 2026 / reviewers in the wild / expert
Youliang Zhong
dblp:41/7526
· DBLP profile ↗
6ranked-venue papers
4as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 3 · 2 first-authorSoftware engineering, systems software and programming languages · 2 · 2 first-authorArtificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1
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
1 paper |
Web and social media mining · 50% Recommender systems · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Recommender systems › social recommendation
social matrix factorization |
0.2 | 1 | 2014 | Trust Prediction with Propagation and Similarity Regularization · AAAI 2014 |
Web and social media mining › social network analysis
trust prediction |
0.2 | 1 | 2014 | Trust Prediction with Propagation and Similarity Regularization · AAAI 2014 |
Methods — techniques the papers use, named apart from their topics
similarity regularization · 0.2propagation regularization · 0.2matrix factorization · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Trust Prediction with Propagation and Similarity RegularizationabstractOnline social networks have been used for a variety of rich activities in recent years, such as investigating potential employees and seeking recommendations of high quality services and service providers. In such activities, trust is one of the most critical factors for the decision-making of users. In the literature, the state-of-the-art trust prediction approaches focus on either dispositional trust tendency and propagated trust of the pair-wise trust relationships along a path or the similarity of trust rating values. However, there are other influential factors that should be taken into account, such as the similarity of the trust rating distributions. In addition, tendency, propagated trust and similarity are of different types, as either personal properties or interpersonal properties. But the difference has been neglected in existing models. Therefore, in trust prediction, it is necessary to take all the above factors into consideration in modeling, and process them separately and differently. In this paper we propose a new trust prediction model based on trust decomposition and matrix factorization, considering all the above influential factors and differentiating both personal and interpersonal properties. In this model, we first decompose trust into trust tendency and tendency-reduced trust. Then, based on tendency-reduced trust ratings, matrix factorization with a regularization term is leveraged to predict the tendency-reduced values of missing trust ratings, incorporating both propagated trust and the similarity of users' rating habits. In the end, the missing trust ratings are composed with predicted tendency-reduced values and trust tendency values. Experiments conducted on a real-world dataset illustrate significant improvement delivered by our approach in trust prediction accuracy over the state-of-the-art approaches. Xiaoming Zheng, Yan Wang 0002, Mehmet A. Orgun, Youliang Zhong, Guanfeng Liu 0001 |
AAAI | 4 |
| 2013 | Optimization of XML Queries by Using Semantics in XML Schemas and the Document Structure
Dung Xuan Thi Le, Moad Maghaydah, Mehmet A. Orgun, Youliang Zhong |
WISE (1) | 4 |
| 2013 | Learning Social Relationship Strength via Matrix Co-Factorization with Multiple Kernels
Youliang Zhong, Lan Du 0002, Jian Yang 0001 |
WISE (1) | 1 |
| 2013 | KPMCF: A Learning Model for Measuring Social Relationship Strength
Youliang Zhong, Xiaoming Zheng, Jian Yang 0001, Mehmet A. Orgun, Yan Wang 0002 |
WISE (2) | 1 |
| 2011 | Personal-Hosting RESTful Web Services for Social Network Based Recommendation
Youliang Zhong, Weiliang Zhao, Jian Yang 0001 |
ICSOC | 1 |
| 2009 | Contract-First Design Techniques for Building Enterprise Web ServicesabstractBased on real development experience, the paper presents a collection of design techniques for building enterprise web services. By applying the techniques to web services development, not only the development increases reusability and productivity, but also the web services improve agility and compatibility.Enterprise web services require high grade of competency in designing web service contracts. A contract of web service formalizes an agreement between web service provider and consumer, in the forms of WSDLs, service schemas and policies. Though contract-first method provides great potential of directly dealing with the contracts, and a number of articles have been published regarding designing WS and XML schemas, however it is still hard for developers to find cookbooks or guidelines concentrated on designing web service contracts with contract-first method. To fill the gap, a set of design techniques are introduced and deployed in practice, incorporating some best practices scattered over the web services community. These techniques cover most of the key aspects of web service, including consolidating service schemas in line with business entities, constructing coarse-grained namespaces, applying versioning over WSDLs and service schemas, and writing fine-grained filters with contracts. Youliang Zhong, Jian Yang 0001 |
ICWS | 1 |