-
Braun, M.: Lesezeichen zum Stöbern : "Social bookmark"-Seiten setzen auf die Empfehlungen ihrer Nutzer (2007)
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- Content
- "Irgendwann ist es einfach zu viel Information. Einmal "Schokolade" bei Google eingeben und mal eben 9 870 000 Links zur Auswahl. "Pilgern - 800 000 Einträge. 21400 000 für "Hörbuch. Die Auswahl fällt schwer, und wer hat schon die Geduld, sich alle Seiten anzugucken. Am besten wäre es, irgendjemand könnte eine der, vielen Seiten empfehlen. Auf eben dieses Prinzip setzen immer mehr Internet-Seiten. Seiten wie www.mister-wong.de bestehen nur aus Empfehlungen von Nutzern für Nutzer. Immerhin vier empfehlen die Webseite zum Pralinenclub, acht Nutzer haben sich für www.theobroma-cacao.de ein Lesezeichen angelegt, beim Pilgern sind sich auch gleich einige Benutzer einig, welche Seite sie am liebsten zum Thema lesen und empfehlen. "Social bookmarks" - Lesezeichen, die man anderen zur Verfügung stellt - sind vor allem auf englischsprachigen Seiten zu finden. Mit Anbietern wie "Mister Wong" oder "Netselektor" können jetzt auch die Deutschen ihre Lieblingslesezeichen im Internet mit anderen teilen. "Bei den großen Suchmaschinen haben es gute Seiten oft schwer: Wenn sie nicht bei Yahoo oder Google nach der Suchanfrage ganz oben stehen, findet sie niemand", sagt "Mister Wong"Pressesprecher Christian Clawien. Für ihn ist das Konzept der "Social bookmarks" die ideale Alternative zur mechanischen Suchmaschine. Noch sind die Zahlen der Aktiven aber gering. 1,3 Millionen abgespeicherte Bookmarks verzeichnet "Mister Wong" seit der Gründung im März 2006. Vor allem Leute, die sowieso bereits einen guten Draht zum Internet haben, nutzten das Angebot, sagt Clawien. Langsam beginnt die Phase, wo Monetarisierung möglich ist." Langfristig soll auch Werbung auf der Seite erscheinen. Bei "www.netselektor.de", im November 2006 gegründet, sitzt zudem noch eine Redaktion vor dem Computer, die die abgelegten Lesezeichen der Nutzer durchforstet und die besten Empfehlungen noch mal als qualitativ hochwertig vorstellt. Nach und nach soll so in Zusammenarbeit mit den Usern ein "Best-of-Internet" entstehen. Natürlich nur mit den Internet-Juwelen, die einer Empfehlung würdig sind. Allerdings erreichen die "Social bookmark"-Seiten auch schnell ihre Grenzen: Nicht alle Stichworte bringen Ergebnisse, nicht immer sind die Vorlieben der Nutzer für Internet-Seiten nachvollziehbar, und noch reicht auch nicht die Anzahl der beteiligten Nutzer, um tatsächlich all die verborgenen Juwelen im riesigen weltweiten Netz zutage zu fördern. Originelles gibt es aber trotzdem schon jetzt - vom Karaoke Trainer bis zu www.dontclick.it", die Seite, die ohne Maus funktionieren soll."
- Date
- 3. 5.1997 8:44:22
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Xie, H.; Li, X.; Wang, T.; Lau, R.Y.K.; Wong, T.-L.; Chen, L.; Wang, F.L.; Li, Q.: Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomy (2016)
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- Abstract
- In recent years, there has been a rapid growth of user-generated data in collaborative tagging (a.k.a. folksonomy-based) systems due to the prevailing of Web 2.0 communities. To effectively assist users to find their desired resources, it is critical to understand user behaviors and preferences. Tag-based profile techniques, which model users and resources by a vector of relevant tags, are widely employed in folksonomy-based systems. This is mainly because that personalized search and recommendations can be facilitated by measuring relevance between user profiles and resource profiles. However, conventional measurements neglect the sentiment aspect of user-generated tags. In fact, tags can be very emotional and subjective, as users usually express their perceptions and feelings about the resources by tags. Therefore, it is necessary to take sentiment relevance into account into measurements. In this paper, we present a novel generic framework SenticRank to incorporate various sentiment information to various sentiment-based information for personalized search by user profiles and resource profiles. In this framework, content-based sentiment ranking and collaborative sentiment ranking methods are proposed to obtain sentiment-based personalized ranking. To the best of our knowledge, this is the first work of integrating sentiment information to address the problem of the personalized tag-based search in collaborative tagging systems. Moreover, we compare the proposed sentiment-based personalized search with baselines in the experiments, the results of which have verified the effectiveness of the proposed framework. In addition, we study the influences by popular sentiment dictionaries, and SenticNet is the most prominent knowledge base to boost the performance of personalized search in folksonomy.
-
Broughton, V.: Automatic metadata generation : Digital resource description without human intervention (2007)
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- Date
- 22. 9.2007 15:41:14
-
Peters, I.: Folksonomies und kollaborative Informationsdienste : eine Alternative zur Websuche? (2011)
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- Pages
- S.29-53
-
Catarino, M.E.; Baptista, A.A.: Relating folksonomies with Dublin Core (2008)
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- Pages
- S.14-22
- Source
- Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas
-
Wesch, M.: Information R/evolution (2006)
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- Date
- 5. 1.2008 19:22:48
-
Morrison, P.J.: Tagging and searching : search retrieval effectiveness of folksonomies on the World Wide Web (2008)
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- Date
- 1. 8.2008 12:39:22
-
Kim, H.L.; Scerri, S.; Breslin, J.G.; Decker, S.; Kim, H.G.: ¬The state of the art in tag ontologies : a semantic model for tagging and folksonomies (2008)
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- Source
- Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas
-
Hayman, S.; Lothian, N.: Taxonomy directed folksonomies : integrating user tagging and controlled vocabularies for Australian education networks (2007)
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- Date
- 26.12.2011 13:29:46
-
Yi, K.; Chan, L.M.: Linking folksonomy to Library of Congress subject headings : an exploratory study (2009)
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- Date
- 20. 6.2010 14:29:15
