Analysis of tag-based recommendation performance for a semantic wiki
|Analysis of tag-based recommendation performance for a semantic wiki|
|Author(s)||Durao F., Dolog P.|
|Published in||CEUR Workshop Proceedings|
|Keyword(s)||Adaptation, Performance, Recommendation, Tags, Wiki (Extra: Adaptation, Performance, Recommendation, Tags, Wiki, Websites, Semantic Web)|
|Article||BASE, CiteSeerX, Google Scholar|
|Web||Ask, Bing, Google (PDF), Yahoo!|
|Download and mirrors|
|Local copy||Not available|
|Remote mirror(s)||Not available|
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of conference papers|
Recommendations play a very important role for revealing related topics addressed in the wikis beyond the currently viewed page. In this paper, we extend KiWi, a semantic wiki with three different recommendation approaches. The first approach is implemented as a traditional tag-based retrieval, the second takes into account external factors such as tag popularity, tag representativeness and the affinity between user and tag and the third approach recommends pages in grouped by tag. The experiment evaluates the wiki performance in different scenarios regarding the amount of pages, tags and users. The results provide insights for the efficient widget allocation and performance management.
- This section requires expansion. Please, help!
Probably, this publication is cited by others, but there are no articles available for them in WikiPapers.