Details
| Original language | English |
|---|---|
| Title of host publication | The Semantic Web, ISWC 2005 |
| Subtitle of host publication | 4th International Semantic Web Conference, ISWC 2005, Proceedings |
| Pages | 293-307 |
| Number of pages | 15 |
| ISBN (electronic) | 978-3-540-32082-1 |
| Publication status | Published - 2005 |
| Event | 4th International Semantic Web Conference, ISWC 2005 - Galway, Ireland Duration: 6 Nov 2005 → 10 Nov 2005 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 3729 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Recommander algorithms have been quite successfully employed in a variety of scenarios from filtering applications to recommendations of movies and books at Amazon.com. However, all these algorithms focus on single item recommendations and do not consider any more complex recommendation structures. This paper explores how semantically rich complex recommendation structures, represented as RDF graphs, can be exchanged and shared in a distributed social network. After presenting a motivating scenario we define several annotation ontologies we use in order to describe context information on the user's desktop and show how our ranking algorithm can exploit this information. We discuss how social distributed networks and interest groups are specified using extended FOAF vocabulary, and how members of these interest groups share semantically rich recommendations in such a network. These recommendations transport shared context as well as ranking information, described in annotation ontologies. We propose an algorithm to compute these rankings which exploits available context information and show how rankings are influenced by the context received from other users as well as by the reputation of the members of the social network with whom the context is exchanged.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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The Semantic Web, ISWC 2005: 4th International Semantic Web Conference, ISWC 2005, Proceedings. 2005. p. 293-307 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3729 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Semantically rich recommendations in social networks for sharing, exchanging and ranking semantic context
AU - Ghita, Stefania
AU - Nejdl, Wolfgang
AU - Paiu, Raluca
PY - 2005
Y1 - 2005
N2 - Recommander algorithms have been quite successfully employed in a variety of scenarios from filtering applications to recommendations of movies and books at Amazon.com. However, all these algorithms focus on single item recommendations and do not consider any more complex recommendation structures. This paper explores how semantically rich complex recommendation structures, represented as RDF graphs, can be exchanged and shared in a distributed social network. After presenting a motivating scenario we define several annotation ontologies we use in order to describe context information on the user's desktop and show how our ranking algorithm can exploit this information. We discuss how social distributed networks and interest groups are specified using extended FOAF vocabulary, and how members of these interest groups share semantically rich recommendations in such a network. These recommendations transport shared context as well as ranking information, described in annotation ontologies. We propose an algorithm to compute these rankings which exploits available context information and show how rankings are influenced by the context received from other users as well as by the reputation of the members of the social network with whom the context is exchanged.
AB - Recommander algorithms have been quite successfully employed in a variety of scenarios from filtering applications to recommendations of movies and books at Amazon.com. However, all these algorithms focus on single item recommendations and do not consider any more complex recommendation structures. This paper explores how semantically rich complex recommendation structures, represented as RDF graphs, can be exchanged and shared in a distributed social network. After presenting a motivating scenario we define several annotation ontologies we use in order to describe context information on the user's desktop and show how our ranking algorithm can exploit this information. We discuss how social distributed networks and interest groups are specified using extended FOAF vocabulary, and how members of these interest groups share semantically rich recommendations in such a network. These recommendations transport shared context as well as ranking information, described in annotation ontologies. We propose an algorithm to compute these rankings which exploits available context information and show how rankings are influenced by the context received from other users as well as by the reputation of the members of the social network with whom the context is exchanged.
UR - http://www.scopus.com/inward/record.url?scp=33646428620&partnerID=8YFLogxK
U2 - 10.1007/11574620_23
DO - 10.1007/11574620_23
M3 - Conference contribution
AN - SCOPUS:33646428620
SN - 978-3-540-29754-3
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 293
EP - 307
BT - The Semantic Web, ISWC 2005
T2 - 4th International Semantic Web Conference, ISWC 2005
Y2 - 6 November 2005 through 10 November 2005
ER -