Chun Guo

Learn More
In citation network analysis, complex behavior is reduced to a simple edge, namely, node A cites node B. The implicit assumption is that A is giving credit to, or acknowledging, B. It is also the case that the contributions of all citations are treated equally, even though some citations appear multiply in a text and others appear only once. In this study,(More)
In this article, we use innovative full-text citation analysis along with supervised topic modeling and network-analysis algorithms to enhance classical bibliometric analysis and publication/author/venue ranking. By utilizing citation contexts extracted from a large number of full-text publications, each citation or publication is represented by a(More)
Citation relationship between scientific publications has been successfully used for scholarly bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relations from various viewpoints, most of them share the same assumption that, if(More)
The goal of this paper is to use innovative text and graph mining algorithms along with full-text citation analysis and topic modeling to enhance classical bibliometric analysis and publication ranking. By utilizing citation contexts extracted from a large number of full-text publications, each citation or publication is represented by a probability(More)
The sheer volume of scholarly publications available online significantly challenges how scholars retrieve the new information available and locate the candidate reference papers. While classical text retrieval and pseudo relevance feedback (PRF) algorithms can assist scholars in accessing needed publications, in this study, we propose an innovative(More)
The fragility and interconnectivity of the planet argue compellingly for a greater understanding of how different communities make sense of their world. One of such critical demands relies on comparing the Chinese and the rest of the world (e.g., Americans), where communities' ideological and cultural backgrounds can be significantly different. While(More)
Online music streaming services (MSS) experienced exponential growth over the past decade. The giant MSS providers not only built massive music collection with metadata, they also accumulated large amount of heterogeneous data generated from users, e.g. listening history, comment, bookmark, and user generated playlist. While various kinds of user data can(More)
In the past decade, online music streaming services (MSS), e.g. Pandora and Spotify, experienced exponential growth. The sheer volume of music collection makes music recommendation increasingly important and the related algorithms are well-documented. In prior studies, most algorithms employed content-based model (CBM) and/or collaborative filtering (CF)(More)
Scholar metadata has traditionally centered on descriptive representation, which has been used as a foundation for scholarly publication repositories and academic information retrieval systems. In this paper we propose innovative and economic methods of generating knowledge-­‐based structural metadata (Structural Keywords) using a combination of NLP-­‐based(More)