Resource Recommendation in Collaborative Tagging Applications

Gemmell, Jonathan; Schimoler, Thomas; Mobasher, Bamshad; Burke, Robin

doi:10.1007/978-3-642-15208-5_1

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…Results . Figure 6 presents an example of results (Gemmell et al 2010b) using data from CiteU‐Like, 4 a social tagging and publication sharing site, similar to BibSonomy. In this experiment, the hybrid combines six constituent components: Pop ignores the user and merely returns a score based on the popularity of the item.…”

Section: Techniques For Recommendationmentioning

confidence: 99%

“…They have been shown effective in traditional e‐commerce settings, with similar performance to other integrative solutions. In the context of the social web, hybrids have been applied most notably to social annotation systems, especially tag and item recommendation (Gemmell et al 2010a and 2010b).…”

Section: Techniques For Recommendationmentioning

confidence: 99%

“…Algorithm . As one example, consider the linear weighted hybrid described in Gemmell et al (2010b). A schematic of this design is shown in figure 5.…”

Section: Techniques For Recommendationmentioning

confidence: 99%

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Recommendation in the Social Web

Burke¹,

Gemmell²,

Hotho³

et al. 2011

AI Magazine

Self Cite

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Recommender systems are a means of personalizing the presentation of information to ensure that users see the items most relevant to them. The social web has added new dimensions to the way people interact on the Internet, placing the emphasis on user-generated content. Users in social networks create photos, videos and other artifacts, collaborate with other users, socialize with their friends and share their opinions online. This outpouring of material has brought increased attention to recommender systems, as a means of managing this vast universe of content. At the same time, the diversity and complexity of the data has meant new challenges for researchers in recommendation. This article describes the nature of recommendation research in social web applications and provides some illustrative examples of current research directions and techniques. It is difficult to overstate the impact of the social web. This new breed of social applications is reshaping nearly every human activity from the way people watch movies to how they overthrow governments. Facebook allows its members to maintain friendships whether they live next door or on another continent. With Twitter, users from celebrities to ordinary folks can launch their 140 character messages out to a diverse horde of ‘‘followers.” Flickr and YouTube users upload their personal media to share with the world, while Wikipedia editors collaborate on the world’s largest encyclopedia.

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Section: Techniques For Recommendationmentioning

confidence: 99%

Section: Techniques For Recommendationmentioning

confidence: 99%

See 1 more Smart Citation

Recommendation in the Social Web

Burke¹,

Gemmell²,

Hotho³

et al. 2011

AI Magazine

Self Cite

View full text Add to dashboard Cite

show abstract

“…In order to achieve efficient filtering, a similarity value has to be computed between user and item features. Moreover [44] defines information channels used in CF as close to the IR vector-space model. So, for instance [43] proposes the reformulation of the performance prediction problem in the field of IR to that of the RS.…”

Section: Contribution Of Ir For Rsmentioning

confidence: 99%