2010 OntheQualityofInferringInterest

• (Wen et al., 2010) ⇒ Zhen Wen, and Ching-Yung Lin. (2010). “On the Quality of Inferring Interests from Social Neighbors.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835853

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%22On+the+quality+of+inferring+interests+from+social+neighbors%22+2010
• http://portal.acm.org/citation.cfm?id=1835853&preflayout=flat#citedby

Quotes

Author Keywords

User interest modeling; Social networks; Quality

Abstract

This paper intends to provide some insights of a scientific problem: how likely one's interests can be inferred from his/her social connections -- friends, friends' friends, 3-degree friends, etc? Is “Birds of a Feather Flocks Together” a norm? We do not consider the friending activity on online social networking sites. Instead, we conduct this study by implementing a privacy-preserving large distribute social sensor system in a large global IT company to capture the multifaceted activities of 30,000+ people, including communications (e.g., emails, instant messaging, etc.) and Web 2.0 activities (e.g., social bookmarking, file sharing, blogging, etc). These activities occupy the majority of employees' time in work, and thus, provide a high quality approximation to the real social connections of employees in the workplace context. In addition to such “informal networks", we investigated the “formal networks", such as their hierarchical structure, as well as the demographic profile data such as geography, job role, self-specified interests, etc. Because user ID matching across multiple sources on the Internet is very difficult, and most user activity logs have to be anonymized before they are processed, no prior studies could collect comparable multifaceted activity data of individuals. That makes this study unique. In this paper, we present a technique to predict the inference quality by utilizing (1) network analysis and network autocorrelation modeling of informal and formal networks, and (2) regression models to predict user interest inference quality from network characteristics. We verify our findings with verify our findings with experiments on both implicit user interests indicated by the content of communications or Web 2.0 activities, and explicit user interests specified in user profiles. We demonstrate that the inference quality prediction increases the inference quality of implicit interests by 42.8%, and inference quality of explicit interests by up to 101%.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 OntheQualityofInferringInterest	Zhen Wen Ching-Yung Lin			On the Quality of Inferring Interests from Social Neighbors		KDD-2010 Proceedings	http://smallblue.research.ibm.com/publications/kdd2010-wen.pdf	10.1145/1835804.1835853		2010