| Author | Title | Year | Journal/Proceedings | Reftype | DOI/URL |
|---|---|---|---|---|---|
| Benz, D., Eisterlehner, F., Hotho, A., Jäschke, R., Krause, B. & Stumme, G. | Managing publications and bookmarks with BibSonomy | 2009 | HT '09: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia, pp. 323-324 | inproceedings | DOI URL |
| Abstract: In this demo we present BibSonomy, a social bookmark and publication sharing system. | |||||
BibTeX:
@inproceedings{benz2009managing,
author = {Benz, Dominik and Eisterlehner, Folke and Hotho, Andreas and Jäschke, Robert and Krause, Beate and Stumme, Gerd},
title = {Managing publications and bookmarks with BibSonomy},
booktitle = {HT '09: Proceedings of the 20th ACM Conference on Hypertext and Hypermedia},
publisher = {ACM},
year = {2009},
pages = {323--324},
url = {http://www.kde.cs.uni-kassel.de/pub/pdf/benz2009managing.pdf},
doi = {http://dx.doi.org/10.1145/1557914.1557969}
}
|
|||||
| Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L. & Stumme, G. | Tag Recommendations in Social Bookmarking Systems | 2008 | AI Communications Vol. 21(4), pp. 231-247 |
article | DOI URL |
| Abstract: Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.
this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of er-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender. |
|||||
BibTeX:
@article{jaeschke2008tag,
author = {Jäschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd},
title = {Tag Recommendations in Social Bookmarking Systems},
journal = {AI Communications},
publisher = {IOS Press},
year = {2008},
volume = {21},
number = {4},
pages = {231-247},
url = {http://dx.doi.org/10.3233/AIC-2008-0438},
doi = {http://dx.doi.org/10.3233/AIC-2008-0438}
}
|
|||||
| Krause, B., Jäschke, R., Hotho, A. & Stumme, G. | Logsonomy - Social Information Retrieval with Logdata | 2008 | HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia, pp. 157-166 | inproceedings | DOI URL |
| Abstract: Social bookmarking systems constitute an established
rt of the Web 2.0. In such systems ers describe bookmarks by keywords lled tags. The structure behind these social stems, called folksonomies, can be viewed a tripartite hypergraph of user, tag and resource des. This underlying network shows ecific structural properties that explain its owth and the possibility of serendipitous ploration. day’s search engines represent the gateway retrieve information from the World Wide b. Short queries typically consisting of o to three words describe a user’s information ed. In response to the displayed sults of the search engine, users click on e links of the result page as they expect e answer to be of relevance. is clickdata can be represented as a folksonomy which queries are descriptions of icked URLs. The resulting network structure, ich we will term logsonomy is very milar to the one of folksonomies. In order find out about its properties, we analyze e topological characteristics of the tripartite pergraph of queries, users and bookmarks a large snapshot of del.icio.us and query logs of two large search engines. l of the three datasets show small world operties. The tagging behavior of users, ich is explained by preferential attachment the tags in social bookmark systems, is flected in the distribution of single query rds in search engines. We can conclude at the clicking behaviour of search engine ers based on the displayed search results d the tagging behaviour of social bookmarking ers is driven by similar dynamics. |
|||||
BibTeX:
@inproceedings{krause2008logsonomy,
author = {Krause, Beate and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd},
title = {Logsonomy - Social Information Retrieval with Logdata},
booktitle = {HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia},
publisher = {ACM},
year = {2008},
pages = {157--166},
url = {http://portal.acm.org/citation.cfm?id=1379092.1379123&coll=ACM&dl=ACM&type=series&idx=SERIES399&part=series&WantType=Journals&title=Proceedings%20of%20the%20nineteenth%20ACM%20conference%20on%20Hypertext%20and%20hypermedia},
doi = {http://doi.acm.org/10.1145/1379092.1379123}
}
|
|||||
Created by JabRef on 25/04/2024.