2014 ClusCiteEffectiveCitationRecomm

• (Ren et al., 2014) ⇒ Xiang Ren, Jialu Liu, Xiao Yu, Urvashi Khandelwal, Quanquan Gu, Lidan Wang, and Jiawei Han. (2014). “ClusCite: Effective Citation Recommendation by Information Network-based Clustering.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623630

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+ClusCite%3A+Effective+Citation+Recommendation+by+Information+Network-based+Clustering
• http://dl.acm.org/citation.cfm?id=2623330.2623630&preflayout=flat#citedby

Quotes

Author Keywords

• Citation behavioral pattern; citation recommendation; clustering; heterogeneous information network; information search and retrieval

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 ClusCiteEffectiveCitationRecomm	Quanquan Gu Xiao Yu Jialu Liu Xiang Ren Urvashi Khandelwal Lidan Wang Jiawei Han			ClusCite: Effective Citation Recommendation by Information Network-based Clustering				10.1145/2623330.2623630		2014