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Follow Recommendation in Communities

  • Chapter
Social Network-Based Recommender Systems

Abstract

Follower networks provide means for informal information propagation. In this chapter we introduce an approach for recommending relevant users to follow. Our approach is based on the automatic analysis of user behavior and network structure. Link-analysis techniques such as PageRank and HITS provide the basis for a novel recommendation model.

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Notes

  1. 1.

    The algorithm introduced by Kleinberg [32] to compute the scores is called Hyperlink Induced Topic Search (HITS).

  2. 2.

    http://developer.github.com/v3/.

  3. 3.

    Event Types: 1 PushEvent, 2 CreateEvent, 3 WatchEvent, 4 IssueCommentEvent, 5 IssuesEvent, 6 ForkEvent, 7 PullRequestEvent, 8 GistEvent, 9 FollowEvent, 10 GollumEvent, 11 CommitCommentEvent, 12 PullRequestReviewCommentEvent, 13 MemberEvent, 14 DeleteEvent, 15 DownloadEvent, 16 PublicEvent, 17 ForkApplyEvent.

  4. 4.

    Location information is valid if the location can be mapped correctly to a geographic place. GitHub users may provide false location information which we are unable to control or validate.

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  1. Siemens Corporate Technology, Wien, Austria

    Daniel Schall

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Schall, D. (2015). Follow Recommendation in Communities. In: Social Network-Based Recommender Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-22735-1_3

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  • DOI: https://doi.org/10.1007/978-3-319-22735-1_3

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22734-4

  • Online ISBN: 978-3-319-22735-1

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