Effective citation recommendation by information network-based clustering

Ren, Xiang

Permalink

https://hdl.handle.net/2142/90594 Copy

Description

Title

Effective citation recommendation by information network-based clustering

Author(s)

Ren, Xiang

Issue Date

2016-04-21

Director of Research (if dissertation) or Advisor (if thesis)

Han, Jiawei

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Citation Recommendation
• Information Network
• Clustering
• Bibliographic Database

Abstract

Citation recommendation is an interesting but challenging research problem. Most existing studies assume that all papers adopt the same criterion and follow the same behavioral pattern in deciding relevance and authority of a paper. However, in reality, papers have distinct citation behavioral patterns when looking for different references, depending on paper content, authors and target venues. In this study, we investigate the problem in the context of heterogeneous bibliographic networks and propose a novel cluster-based citation recommendation framework, called ClusCite, which explores the principle that citations tend to be softly clustered into interest groups based on multiple types of relationships in the network. Therefore, we predict each query's citations based on related interest groups, each having its own model for paper authority and relevance. Specifically, we learn group memberships for objects and the significance of relevance features for each interest group, while also propagating relative authority between objects, by solving a joint optimization problem. Experiments on both DBLP and PubMed datasets demonstrate the power of the proposed approach, with 17.68% improvement in Recall@50 and 9.57% growth in MRR over the best performing baseline.

Graduation Semester

2016-05

Type of Resource

text

Permalink

http://hdl.handle.net/2142/90594

Copyright and License Information

Copyright 2016 Xiang Ren

Owning Collections