Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Introduction to Social Information Access Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Accessing Information with Tags: Search and Ranking

Authors : Beate Navarro Bullock, Andreas Hotho, Gerd Stumme

Published in: Social Information Access

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

With the growth of the Social Web, a variety of new web-based services arose and changed the way users interact with the internet and consume information. One central phenomenon was and is tagging which allows to manage, organize and access information in social systems. Tagging helps to manage all kinds of resources, making their access much easier. The first type of social tagging systems were social bookmarking systems, i.e., platforms for storing and sharing bookmarks on the web rather than just in the browser. Meanwhile, (hash-)tagging is central in many other Social Media systems such as social networking sites and micro-blogging platforms. To allow for efficient information access, special algorithms have been developed to guide the user, to search for information and to rank the content based on tagging information contributed by the users.
In this article we review several aspects of the tagging process and its role for accessing information using search and ranking in tagging systems. A literature review of existing work in this area will be complemented by case studies which showcase findings of our own research. We will start with discussing typical properties of tagging systems, present example systems and their typical functionality, their strengths and weaknesses, the users’ motivations, and different types of tags and annotators. To get an understanding of search and ranking methods, we use the formalization of tagging systems as a tripartite graph of users, tags, and resources – known as folksonomy – and discuss its network properties.
Ranking in folksonomies is a core component of information access in such systems. We review two central algorithms, FolkRank and Adjusted Hits, before focussing on a tighter integration of Web search and folksonomies. For this, we compare search in standard search engines with tag-based search, review Social PageRank, a method for ranking web pages that is using the information of tagging systems, and discuss learning-to-rank methods which also utilize tags to improve the ranking of web pages. Finally, we present the concept of logsonomies which provide a unified view on search and tagging by considering clicks on search results as an implicit tagging process. Concluding, we discuss future options for a tighter integration of tagging and search with the goal of improving information access based on user provided content.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Network-Based Social Search
next chapter Rating-Based Collaborative Filtering: Algorithms and Evaluation
Footnotes
3
http://​del.​icio.​us (as of June 2017, the service stopped).
 
4
http://​www.​connotea.​org/​ (as of March 2013, the service stopped).
 
20
See also Sect. 1 of Chap. 6 of this book [28].
 
Literature
1.
Abel, F.: Contextualization, user modeling and personalization in the social web: from social tagging via context to cross-system user modeling and personalization. Ph.D. thesis, University of Hanover (2011). http://​d-nb.​info/​1014252423
2.
Abel, F., Baldoni, M., Baroglio, C., Henze, N., Kawase, R., Krause, D., Patti, V.: Leveraging search and content exploration by exploiting context in folksonomy systems. New Rev. Hypermedia Multimed. 16(1–2), 33–70 (2010)CrossRef
3.
Abel, F., Henze, N., Krause, D.: Ranking in folksonomy systems: can context help? In: Shanahan, J.G., Amer-Yahia, S., Manolescu, I., Zhang, Y., Evans, D.A., Kolcz, A., Choi, K.-S., Chowdhury, A. (eds.) Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM 2008, Napa Valley, California, USA, 26–30 October 2008, pp. 1429–1430. ACM (2008)
4.
5.
Adar, E.: User 4xxxxx9: anonymizing query logs. In: Query Logs Workshop at WWW 2006 (2007)
6.
Al-Khalifa, H.S., Davis, H.C.: Towards better understanding of folksonomic patterns. In: Proceedings of the Eighteenth Conference on Hypertext and Hypermedia, HT 2007, pp. 163–166. ACM, New York (2007)
7.
Amaral, L.A.N., Scala, A., Barthelemy, M., Stanley, H.: Classes of small-world networks. Proc. Nat. Acad. Sci. USA 97, 11149–11152 (2000)CrossRef
8.
Bao, S., Xue, G., Wu, X., Yu, Y., Fei, B., Su, Z.: Optimizing web search using social annotations. In: Proceedings of the WWW 2007, Banff, Canada, pp. 501–510 (2007)
9.
10.
Barabási, A.-L., Albert, R., Jeong, H.: Scale-free characteristics of random networks: the topology of the world-wide web. Phys. A Stat. Mech. Appl. 281(1–4), 69–77 (2000)CrossRef
11.
Barabasi, A.-L., Bonabeau, E.: Scale-free networks. Sci. Am. 288, 60–69 (2003)CrossRef
12.
Belém, F.M., Martins, E.F., Almeida, J.M., Gonçalves, M.A.: Personalized and object-centered tag recommendation methods for web 2.0 applications. Inf. Proc. Manag. 50(4), 524–553 (2014)CrossRef
13.
Benz, D., Eisterlehner, F., Hotho, A., Jäschke, R., Krause, B., Stumme, G.: Managing publications and bookmarks with bibsonomy. In: Cattuto, C., Ruffo, G., Menczer, F. (eds.) Proceedings of the 20th ACM Conference on Hypertext and Hypermedia, HT 2009, pp. 323–324. ACM, New York , June 2009
14.
Benz, D., Hotho, A., Jäschke, R., Krause, B., Mitzlaff, F., Schmitz, C., Stumme, G.: The social bookmark and publication management system bibsonomy. VLDB J. 19(6), 849–875 (2010)CrossRef
15.
Biancalana, C., Gasparetti, F., Micarelli, A., Sansonetti, G.: Social semantic query expansion. ACM Trans. Intell. Syst. Technol. (TIST) 4(4), 60 (2013)
16.
17.
Bogers, T.: Tag-based recommendation. In: Brusilovsky, P., He, D. (eds.) Social Information Access, LNCS, 10100, pp. 441–479. Springer, Heidelberg (2018)
18.
Bouadjenek, M.R., Hacid, H., Bouzeghoub, M.: SoPRa: a new social personalized ranking function for improving web search. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2013, pp. 861–864. ACM, New York (2013)
19.
20.
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1–7), 107–117 (1998)CrossRef
21.
Brusilovsky, P., He, D.: Introduction to social information access. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 1–18. Springer, Cham (2018)
22.
Brusilovsky, P., Smyth, B., Shapira, B.: Social search. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 213–276. Springer, Cham (2018)
23.
24.
Cattuto, C., Loreto, V., Pietronero, L.: Collaborative tagging and semiotic dynamics. CoRR, abs/cs/0605015 (2006)
25.
Cattuto, C., Schmitz, C., Baldassarri, A., Servedio, V.D.P., Loreto, V., Hotho, A., Grahl, M., Stumme, G.: Network properties of folksonomies. AI Commun. J. 20(4), 245–262 (2007). Special Issue on “Network Analysis in Natural Sciences and Engineering”MathSciNet
26.
Cunha, E., Magno, G., Comarela, G., Almeida, V., Gonçalves, M.A., Benevenuto, F.: Analyzing the dynamic evolution of hashtags on twitter: a language-based approach. In: Proceedings of the Workshop on Languages in Social Media, pp. 58–65. Association for Computational Linguistics (2011)
27.
Dellschaft, K., Staab, S.: An epistemic dynamic model for tagging systems. In: Proceedings of the Nineteenth ACM Conference on Hypertext and hypermedia, HT 2008, pp. 71–80. ACM, New York (2008)
28.
Dimitrov, D., Helic, D., Strohmaier, M.: Tag-based navigation and visualization. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 181–212. Springer, Cham (2018)
29.
Djuana, E., Xu, Y., Li, Y., Jøsang, A.: A combined method for mitigating sparsity problem in tag recommendation. In: 47th Hawaii International Conference on System Sciences, HICSS 2014, Waikoloa, HI, USA, 6–9 January 2014, pp. 906–915 (2014)
30.
Doerfel, S., Jäschke, R., Stumme, G.: The role of cores in recommender benchmarking for social bookmarking systems. ACM Trans. Intell. Syst. Technol. 7(3), 40:1–40:33 (2016)CrossRef
31.
Doerfel, S., Zöller, D., Singer, P., Niebler, T., Hotho, A., Strohmaier, M.: Of course we share! testing assumptions about social tagging systems. CoRR, abs/1401.0629 (2014)
32.
Doerfel, S., Zoller, D., Singer, P., Niebler, T., Hotho, A., Strohmaier, M.: What users actually do in a social tagging system: a study of user behavior in bibsonomy. ACM Trans. Web 10(2), 14:1–14:32 (2016)CrossRef
33.
Dong, X., Chen, X., Guan, Y., Yu, Z., Li, S.: An overview of learning to rank for information retrieval. In: Burgin, M., Chowdhury, M.H., Ham, C.H., Ludwig, S.A., Su, W., Yenduri, S. (eds.) CSIE (3), pp. 600–606. IEEE Computer Society (2009)
34.
Dou, Z., Song, R., Yuan, X., Wen, J.-R.: Are click-through data adequate for learning web search rankings? In: Proceeding of the 17th ACM Conference on Information and Knowledge Management, CIKM 2008, pp. 73–82. ACM, New York (2008)
35.
Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. In: Proceedings of the Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, SIGCOMM 1999, pp. 251–262. ACM, New York (1999)
36.
Ferragina, P., Piccinno, F., Santoro, R.: On analyzing hashtags in Twitter. In: Ninth International AAAI Conference on Web and Social Media (2015)
37.
38.
Gao, Y., Wang, M., Zha, Z.J., Shen, J., Li, X., Wu, X.: Visual-textual joint relevance learning for tag-based social image search. IEEE Trans. Image Process. 22(1), 363–376 (2013)MathSciNetCrossRefMATH
39.
Golder, S., Huberman, B.A.: The structure of collaborative tagging systems, August 2005
40.
Golder, S., Huberman, B.A.: The structure of collaborative tagging systems. J. Inf. Sci. 32(2), 198–208 (2006)CrossRef
41.
Golder, S.A., Huberman, B.A.: Usage patterns of collaborative tagging systems. J. Inf. Sci. 32, 198–208 (2006)CrossRef
42.
43.
Gupta, M., Li, R., Yin, Z., Han, J.: Survey on social tagging techniques. SIGKDD Explor. Newsl. 12, 58–72 (2010)CrossRef
44.
Halpin, H., Robu, V., Shepard, H.: The dynamics and semantics of collaborative tagging. In: Proceedings of the 1st Semantic Authoring and Annotation Workshop (SAAW 2006), pp. 211–220 (2006)
45.
Halpin, H., Robu, V., Shepherd, H.: The complex dynamics of collaborative tagging. In: Proceedings of the 16th International Conference on World Wide Web, WWW 2007, pp. 211–220. ACM, New York (2007)
46.
Heymann, P., Koutrika, G., Molina, H.: Can social bookmarking improve web search? In: Proceedings of the International Conference on Web Search and Web Data Mining, WSDM 2008, pp. 195–206. ACM, Palo Alto (2008)
47.
Heymans, M.: Introducing Google social search: i finally found my friend’s New York blog! (2009)
48.
49.
Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: BibSonomy: a social bookmark and publication sharing system. In: de Moor, A., Polovina, S., Delugach, H. (eds.) Proceedings of the Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures, Aalborg, Denmark. Aalborg University Press, July 2006
50.
51.
Ignatov, D., Zhuk, R., Konstantinova, N.: Learning hypotheses from triadic labeled data. In: 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), vol. 2, pp. 474–480, August 2014
52.
53.
Joachims, T.: Optimizing search engines using clickthrough data. In: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 133–142 (2002)
54.
Jäschke, R., Eisterlehner, F., Hotho, A., Stumme, G.: Testing and evaluating tag recommenders in a live system. In: Proceedings of the Third ACM Conference on Recommender Systems, RecSys 2009, pp. 369–372. ACM, New York (2009)
55.
Kammerer, Y., Nairn, R., Pirolli, P., Chi, E.H.: Signpost from the masses: learning effects in an exploratory social tag search browser. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2009, pp. 625–634. ACM, New York (2009)
56.
57.
58.
Kolay, S., Dasdan, A.: The value of socially tagged URLs for a search engine. In: Quemada, J., Leon, G., Maarek, Y.S., Nejdl, W. (eds.) WWW, pp. 1203–1204. ACM (2009)
59.
60.
Krause, B., Jäschke, R., Hotho, A., Stumme, G.: Logsonomy - social information retrieval with logdata. In: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia, HT 2008, pp. 157–166. ACM, New York (2008)
61.
62.
Körner, C., Benz, D., Strohmaier, M., Hotho, A., Stumme, G.: Stop thinking, start tagging - tag semantics emerge from collaborative verbosity. In: Proceedings of the 19th International World Wide Web Conference (WWW 2010). ACM, Raleigh, April 2010
63.
Körner, C., Kern, R., Grahsl, H.-P., Strohmaier, M.: Of categorizers and describers: an evaluation of quantitative measures for tagging motivation. In: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, HT 2010, pp. 157–166. ACM, New York (2010)
64.
Lancaster, F.W.: Indexing and Abstracting in Theory and Practice. University of Illinois, Chicago, Graduate School of Library and Information Science (2003)
65.
66.
Lee, K.-P., Kim, H.-G., Kim, H.-J.: A social inverted index for social-tagging-based information retrieval. J. Inf. Sci. 38(4), 313–332 (2012)CrossRef
67.
68.
Li, P., Nie, J.-Y., Wang, B., He, J.: Document re-ranking using partial social tagging. In: 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 274–281, December 2012
69.
Li, X., Guo, L., Zhao, Y.E.: Tag-based social interest discovery. In: Proceedings of the 17th International Conference on World Wide Web, WWW 2008, pp. 675–684. ACM, New York (2008)
70.
Lin, Y., Lin, H., Jin, S., Ye, Z.: Social annotation in query expansion: a machine learning approach. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, pp. 405–414. ACM, New York (2011)
71.
Lipczak, M., Milios, E.: The impact of resource title on tags in collaborative tagging systems. In: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, HT 2010, pp. 179–188. ACM, New York (2010)
72.
Macdonald, C., Ounis, I.: Usefulness of quality click-through data for training. In: Proceedings of the 2009 Workshop on Web Search Click Data, WSCD 2009, pp. 75–79. ACM, New York (2009)
73.
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)CrossRefMATH
74.
75.
Markines, B., Cattuto, C., Menczer, F.: Social spam detection. In: Fetterly, D., Gyöngyi, Z. (eds.) Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web AIRWeb, ACM International Conference Proceeding Series, pp. 41–48 (2009)
76.
Marlow, C., Naaman, M., Boyd, D., Davis, M.: HT06, tagging paper, taxonomy, Flickr, academic article, to read. In: Proceedings of the Seventeenth Conference on Hypertext and Hypermedia, pp. 31–40. ACM (2006)
77.
Mathes, A.: Folksonomies-Cooperative Classification and Communication Through Shared Metadata, Computer Mediated Communication, LIS590CMC (Doctoral Seminar). Graduate School of Library and Information Science, University of Illinois Urbana-Champaign, December 2004
78.
79.
Morrison, P.J.: Tagging and searching: search retrieval effectiveness of folksonomies on the world wide web. Inf. Process. Manag. 44, 1562–1579 (2008)CrossRef
80.
Navarro Bullock, B., Jäschke, R., Hotho, A.: Tagging data as implicit feedback for learning-to-rank. In: Proceedings of the ACM WebSci 2011, June 2011
81.
Neubauer, N., Obermayer, K.: Hyperincident connected components of tagging networks. SIGWEB Newsl. 4:1–4:10 (2009)
82.
Neubauer, N., Wetzker, R., Obermayer, K.: Tag spam creates large non-giant connected components. In: Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web, AIRWeb 2009, pp. 49–52. ACM, New York (2009)
83.
Newman, M.: Power laws, Pareto distributions and Zipf’s law. Contemp. Phys. 46(5), 323–351 (2005)CrossRef
84.
Niebler, T., Becker, M., Zoller, D., Doerfel, S., Hotho, A.: FolkTrails: interpreting navigation behavior in a social tagging system. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, CIKM 2016. ACM, New York (2016)
85.
Noll, M.G.: Understanding and leveraging the social web for information retrieval. Ph.D. thesis, Universität Potsdam, April 2010
86.
87.
Overell, S., Sigurbjörnsson, B., van Zwol, R.: Classifying tags using open content resources. In: Proceedings of the Second ACM International Conference on Web Search and Data Mining, WSDM 2009, pp. 64–73. ACM, New York (2009)
88.
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. Technical report 1999–66, Stanford InfoLab, November 1999
89.
90.
91.
Peng, J., Zeng, D.D., Zhao, H., Wang, F.-Y.: Collaborative filtering in social tagging systems based on joint item-tag recommendations. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM 2010, pp. 809–818. ACM, New York (2010)
92.
Petersen, C., Simonsen, J.G., Lioma, C.: Power law distributions in information retrieval. ACM Trans. Inf. Syst. 34(2), 8:1–8:37 (2016)CrossRef
93.
Quintarelli, E.: Folksonomies: power to the people, June 2005
94.
Rendle, S., Schmidt-Thieme, L.: Pairwise interaction tensor factorization for personalized tag recommendation. In: Davison, B.D., Suel, T., Craswell, N., Liu, B. (eds.) WSDM, pp. 81–90. ACM (2010)
95.
Sen, S., Lam, S.K., Rashid, A.M., Cosley, D., Frankowski, D., Osterhouse, J., Harper, F.M., Riedl, J.: Tagging, communities, vocabulary, evolution. In: Proceedings of the 2006 20th Anniversary Conference on Computer Supported Cooperative Work, CSCW 2006, pp. 181–190. ACM, New York (2006)
96.
Spiteri, L.: Structure and form of folksonomy tags: the road to the public library catalogue. Webology 4(2) (2007)
97.
98.
Teevan, J., Ramage, D., Morris, M.R.: # TwitterSearch: a comparison of microblog search and web search. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 35–44. ACM (2011)
99.
Vallet, D., Cantador, I., Jose, J.M.: Personalizing web search with folksonomy-based user and document profiles. In: Gurrin, C., He, Y., Kazai, G., Kruschwitz, U., Little, S., Roelleke, T., Rüger, S., van Rijsbergen, K. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 420–431. Springer, Heidelberg (2010). https://​doi.​org/​10.​1007/​978-3-642-12275-0_​37CrossRef
100.
von Ahn, L., Dabbish, L.: Labeling images with a computer game. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2004, pp. 319–326. ACM, New York (2004)
101.
Wagner, C., Strohmaier, M.: The wisdom in tweetonomies: acquiring latent conceptual structures from social awareness streams. In: Proceedings of the Semantic Search 2010 Workshop (SemSearch 2010), April 2010
102.
Wal, T.V.: Explaining and showing broad and narrow folksonomies. Blog post, February 2005
103.
104.
Watts, D.J., Strogatz, S.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)CrossRefMATH
105.
Wetzker, R., Zimmermann, C., Bauckhage, C.: Analyzing social bookmarking systems: a del.icio.us cookbook. In: Mining Social Data (MSoDa) Workshop Proceedings, ECAI 2008, pp. 26–30, July 2008
106.
Wetzker, R., Zimmermann, C., Bauckhage, C.: Detecting trends in social bookmarking systems: a del. icio. us endeavor. In: Exploring Advances in Interdisciplinary Data Mining and Analytics: New Trends, pp. 34–51 (2011)
107.
108.
Willinge, W., Alderson, D., Doyle, J.C.: Mathematics and the internet: a source of enormous confusion and great potential. Technical report 5, May 2009
109.
Wu, H., Zubair, M., Maly, K.: Harvesting social knowledge from folksonomies. In: Proceedings of the Seventeenth Conference on Hypertext and Hypermedia, HYPERTEXT 2006, pp. 111–114. ACM, New York (2006)
110.
Wu, X., Zhang, L., Yu, Y.: Exploring social annotations for the semantic web. In: Proceedings of the 15th International Conference on World Wide Web, WWW 2006, pp. 417–426. ACM Press, New York (2006)
111.
112.
Yeung, C.M.A.: From user behaviours to collective semantics. Ph.D. thesis, University of Southampton (2009)
113.
Yin, D., Hong, L., Davison, B.D.: Exploiting session-like behaviors in tag prediction. In: Proceedings of the 20th International Conference Companion on World Wide Web, WWW 2011, pp. 167–168. ACM, New York (2011)
114.
Yue, Z., He, D.: Collaborative information search. In: Brusilovsky, P., He, D. (eds.) Social Information Access. LNCS, vol. 10100, pp. 108–141. Springer, Cham (2018)
115.
Zhou, D., Lawless, S., Wade, V.: Improving search via personalized query expansion using social media. Inf. Retr. 15(3–4), 218–242 (2012)CrossRef
116.
Zoller, D., Doerfel, S., Jäschke, R., Stumme, G., Hotho, A.: On publication usage in a social bookmarking system. In: Proceedings of the 2015 ACM Conference on Web Science, WebSci 2015, pp. 67:1–67:2. ACM, New York (2015)
Metadata
Title
Accessing Information with Tags: Search and Ranking
Authors
Beate Navarro Bullock
Andreas Hotho
Gerd Stumme
Copyright Year
2018
Book
Social Information Access

Print ISBN: 978-3-319-90091-9

Electronic ISBN: 978-3-319-90092-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-90092-6_9

Premium Partner

    Image Credits