2012 CrossDomainCollaborationRecomme

• (Tang et al., 2012) ⇒ Jie Tang, Sen Wu, Jimeng Sun, and Hang Su. (2012). “Cross-domain Collaboration Recommendation.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339730

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Cited By

• http://scholar.google.com/scholar?q=%222012%22+Cross-domain+Collaboration+Recommendation
• http://dl.acm.org/citation.cfm?id=2339530.2339730&preflayout=flat#citedby

Quotes

Author Keywords

• Collaboration recommendation; information search and retrieval; social influence; social network

Abstract

Interdisciplinary collaborations have generated huge impact to society. However, it is often hard for researchers to establish such cross-domain collaborations. What are the patterns of cross-domain collaborations? How do those collaborations form? Can we predict this type of collaborations?

Cross-domain collaborations exhibit very different patterns compared to traditional collaborations in the same domain: 1) sparse connection: cross-domain collaborations are rare; 2) complementary expertise: cross-domain collaborators often have different expertise and interest; 3) topic skewness: cross-domain collaboration topics are focused on a subset of topics. All these patterns violate fundamental assumptions of traditional recommendation systems.

In this paper, we analyze the cross-domain collaboration data from research publications and confirm the above patterns. We propose the Cross-domain Topic Learning (CTL) model to address these challenges. For handling sparse connections, CTL consolidates the existing cross-domain collaborations through topic layers instead of at author layers, which alleviates the sparseness issue. For handling complementary expertise, CTL models topic distributions from source and target domains separately, as well as the correlation across domains. For handling topic skewness, CTL only models relevant topics to the cross-domain collaboration.

We compare CTL with several baseline approaches on large publication datasets from different domains. CTL outperforms baselines significantly on multiple recommendation metrics. Beyond accurate recommendation performance, CTL is also insensitive to parameter tuning as confirmed in the sensitivity analysis.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 CrossDomainCollaborationRecomme	Jie Tang Jimeng Sun Sen Wu Hang Su			Cross-domain Collaboration Recommendation				10.1145/2339530.2339730		2012