Discovering shared interests using graph analysis

Schwartz, Michael F.; Wood, David

doi:10.1145/163381.163402

Cited by 161 publications

(91 citation statements)

References 7 publications

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“…In 1993, Schwartz and Wood exploited email for the purpose of inferring common interest relationships between people. Schwartz and Wood (1993) explore graphs produced from email communication patterns to discover shared-interest relationships amongst people and obtain a list of people who share a given interest. The system returns a list of people in no particular order.…”

Section: Expertise Locator Approachesmentioning

confidence: 99%

The design and evaluation of EKE, a semi-automated email knowledge extraction tool

Tedmori

Jackson

2012

Knowledge Management Research & Practice

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Section: Expertise Locator Approachesmentioning

confidence: 99%

The design and evaluation of EKE, a semi-automated email knowledge extraction tool

Tedmori

Jackson

2012

Knowledge Management Research & Practice

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“…Triggered by the introduction of the Enron [12] and W3C [30] collections, opportunities opened up to study new challenges. A large body of these efforts focused on people-related tasks, including name recognition and reference resolution [7,19,20], contact information extraction [1,5], identity modeling and resolution [9], discovery of peoples' roles [16], and finding experts [1,25,34]. Another line of work centers around efficient access to email-based discussion lists.…”

Section: Related Workmentioning

confidence: 99%

Using Contextual Information to Improve Search in Email Archives

Weerkamp

Balog

Rijke

2009

Lecture Notes in Computer Science

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Abstract. In this paper we address the task of finding topically relevant email messages in public discussion lists. We make two important observations. First, email messages are not isolated, but are part of a larger online environment. This context, existing on different levels, can be incorporated into the retrieval model. We explore the use of thread, mailing list, and community content levels, by expanding our original query with term from these sources. We find that query models based on contextual information improve retrieval effectiveness. Second, email is a relatively informal genre, and therefore offers scope for incorporating techniques previously shown useful in searching user-generated content. Indeed, our experiments show that using query-independent features (email length, thread size, and text quality), implemented as priors, results in further improvements.

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“…Some typical problems in Social Network Analysis (SNA) include discovering groups of individuals sharing the same properties using the connectivity properties of networks [16]. Since this does not consider the textual information of the entities, it limits the applications of SNA.…”

Section: Related Workmentioning

confidence: 99%

Connecting Sparsely Distributed Similar Bloggers

Agarwal

Liu

Subramanya

et al. 2009

2009 Ninth IEEE International Conference on Data Mining

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Abstract-The nature of the Blogosphere determines that the majority of bloggers are only connected with a small number of fellow bloggers, and similar bloggers can be largely disconnected from each other. Aggregating them allows for cost-effective personalized services, targeted marketing, and exploration of new business opportunities. As most bloggers have only a small number of adjacent bloggers, the problem of aggregating similar bloggers presents challenges that demand novel algorithms of connecting the non-adjacent due to the fragmented distributions of bloggers. In this work, we define the problem, delineate its challenges, and present an approach that uses innovative ways to employ contextual information and collective wisdom to aggregate similar bloggers. A real-world blog directory is used for experiments. We demonstrate the efficacy of our approach, report findings, and discuss related issues and future work.

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Discovering shared interests using graph analysis

Cited by 161 publications

References 7 publications

The design and evaluation of EKE, a semi-automated email knowledge extraction tool

The design and evaluation of EKE, a semi-automated email knowledge extraction tool

Using Contextual Information to Improve Search in Email Archives

Connecting Sparsely Distributed Similar Bloggers

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