In collaborative tagging systems, it is generally assumed that users assign tags to facilitate retrieval of content at a later time. There is, however, little behavioral evidence that tags actually serve this purpose. Using a large-scale dataset from the social music website Last.fm, we explore how patterns of music tagging and subsequent listening interact to determine if there exist measurable signals of tags functioning as retrieval aids. Specifically, we describe our methods for testing if the assignment of a tag tends to lead to an increase in listening behavior. Results suggest that tagging, on average, leads to only very small increases in listening rates, and overall the data do

@incollection{lorince2015analysis, author = {Lorince, Jared and Joseph, Kenneth and Todd, PeterM.}, title = {Analysis of Music Tagging and Listening Patterns: Do Tags Really Function as Retrieval Aids?}, editor = {Agarwal, Nitin and Xu, Kevin and Osgood, Nathaniel}, booktitle = {Social Computing, Behavioral-Cultural Modeling, and Prediction}, series = {Lecture Notes in Computer Science}, publisher = {Springer International Publishing}, year = {2015}, volume = {9021}, pages = {141-152}, url = {http://dx.doi.org/10.1007/978-3-319-16268-3_15}, doi = {10.1007/978-3-319-16268-3_15}, isbn = {978-3-319-16267-6}, keywords = {folksonomy, last.fm, retrieval, tagging, usage}, abstract = {In collaborative tagging systems, it is generally assumed that users assign tags to facilitate retrieval of content at a later time. There is, however, little behavioral evidence that tags actually serve this purpose. Using a large-scale dataset from the social music website Last.fm, we explore how patterns of music tagging and subsequent listening interact to determine if there exist measurable signals of tags functioning as retrieval aids. Specifically, we describe our methods for testing if the assignment of a tag tends to lead to an increase in listening behavior. Results suggest that tagging, on average, leads to only very small increases in listening rates, and overall the data do } }

%0 = incollection %A = Lorince, Jared and Joseph, Kenneth and Todd, PeterM. %B = Social Computing, Behavioral-Cultural Modeling, and Prediction %D = 2015 %I = Springer International Publishing %T = Analysis of Music Tagging and Listening Patterns: Do Tags Really Function as Retrieval Aids? %U = http://dx.doi.org/10.1007/978-3-319-16268-3_15

@inproceedings{dong2009overview, author = {Dong, Xishuang and Chen, Xiaodong and Guan, Yi and Yu, Zhiming and Li, Sheng}, title = {An Overview of Learning to Rank for Information Retrieval.}, editor = {Burgin, Mark and Chowdhury, Masud H. and Ham, Chan H. and Ludwig, Simone A. and Su, Weilian and Yenduri, Sumanth}, booktitle = {CSIE (3)}, publisher = {IEEE Computer Society}, year = {2009}, pages = {600-606}, url = {http://dblp.uni-trier.de/db/conf/csie/csie2009-3.html#DongCGYL09}, isbn = {978-0-7695-3507-4}, keywords = {information, learning, learning-to-rank, overview, rank, retrieval} }

%0 = inproceedings %A = Dong, Xishuang and Chen, Xiaodong and Guan, Yi and Yu, Zhiming and Li, Sheng %B = CSIE (3) %D = 2009 %I = IEEE Computer Society %T = An Overview of Learning to Rank for Information Retrieval. %U = http://dblp.uni-trier.de/db/conf/csie/csie2009-3.html#DongCGYL09

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.

@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}, address = {New York, NY, USA}, 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}, isbn = {978-1-59593-985-2}, keywords = {folksonomy, information, logsonomy, retrieval, social}, 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.} }

%0 = inproceedings %A = Krause, Beate and Jäschke, Robert and Hotho, Andreas and Stumme, Gerd %B = HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia %C = New York, NY, USA %D = 2008 %I = ACM %T = Logsonomy - Social Information Retrieval with Logdata %U = 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

"Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures." - Publisher's description.

@book{manning2008introduction, author = {Manning, Christopher D. and Raghavan, Prabhakar and Schütze, Hinrich}, title = {Introduction to Information Retrieval}, publisher = {Cambridge University Press}, address = {New York}, year = {2008}, url = {http://www.amazon.com/Introduction-Information-Retrieval-Christopher-Manning/dp/0521865719/ref=sr_1_1?ie=UTF8&qid=1337379279&sr=8-1}, isbn = {9780521865715 0521865719}, keywords = {book, citedBy:doerfel2012leveraging, information, introduction, ir, retrieval}, abstract = {"Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures." -- Publisher's description.} }

%0 = book %A = Manning, Christopher D. and Raghavan, Prabhakar and Schütze, Hinrich %C = New York %D = 2008 %I = Cambridge University Press %T = Introduction to Information Retrieval %U = http://www.amazon.com/Introduction-Information-Retrieval-Christopher-Manning/dp/0521865719/ref=sr_1_1?ie=UTF8&qid=1337379279&sr=8-1

Folksonomies in Wissensrepräsentation und Information Retrieval. Die populären Web 2.0-Dienste werden von Prosumern - Produzenten und gleichsam Konsumenten - nicht nur dazu genutzt, Inhalte zu produzieren, sondern auch, um sie inhaltlich zu erschließen. Folksonomies erlauben es dem Nutzer, Dokumente mit eigenen Schlagworten, sog. Tags, zu beschreiben, ohne dabei auf gewisse Regeln oder Vorgaben achten zu müssen. Neben einigen Vorteilen zeigen Folksonomies aber auch zahlreiche Schwächen (u. a. einen Mangel an Präzision). Um diesen Nachteilen größtenteils entgegenzuwirken, schlagen wir eine Interpretation der Tags als natürlichsprachige Wörter vor. Dadurch ist es uns möglich, Methoden des Natural Language Processing (NLP) auf die Tags anzuwenden und so linguistische Probleme der Tags zu beseitigen. Darüber hinaus diskutieren wir Ansätze und weitere Vorschläge (Tagverteilungen, Kollaboration und akteurspezifische Aspekte) hinsichtlich eines Relevance Rankings von getaggten Dokumenten. Neben Vorschlägen auf ähnliche Dokumente (glqqmore like this!grqq) erlauben Folksonomies auch Hinweise auf verwandte Nutzer und damit auf Communities (glqqmore like me!grqq). Folksonomies in Knowledge Representation and Information Retrieval In Web 2.0 services grqqprosumers” - producers and consumers - collaborate not only for the purpose of creating content, but to index these pieces of information as well. Folksonomies permit actors to describe documents with subject headings, grqqtagsgrqq, without regarding any rules. Apart from a lot of benefits folksonomies have many shortcomings (e.g., lack of precision). In order to solve some of the problems we propose interpreting tags as natural language terms. Accordingly, we can introduce methods of NLP to solve the tags’ linguistic problems. Additionally, we present criteria for tagged documents to create a ranking by relevance (tag distribution, collaboration and actor-based aspects). Besides recommending similar documents (glqqmore like this!grqq) folksonomies can be used for the recommendation of similar users and communities (glqqmore like me!grqq).

@article{PeSt08, author = {Peters, Isabella and Stock, Wolfgang G.}, title = {Folksonomies in Wissensrepräsentation und Information Retrieval}, journal = {Information -- Wissenschaft und Praxis}, year = {2008}, volume = {59 }, number = {2}, pages = {77--90}, url = {http://www.phil-fak.uni-duesseldorf.de/infowiss/admin/public_dateien/files/1/1204547968stock212_h.htm}, issn = {1434-4653}, keywords = {folksonomy, information, ir, retrieval, wissensrepräsentation, übersicht}, abstract = {Folksonomies in Wissensrepräsentation und Information Retrieval. Die populären Web 2.0-Dienste werden von Prosumern -- Produzenten und gleichsam Konsumenten -- nicht nur dazu genutzt, Inhalte zu produzieren, sondern auch, um sie inhaltlich zu erschließen. Folksonomies erlauben es dem Nutzer, Dokumente mit eigenen Schlagworten, sog. Tags, zu beschreiben, ohne dabei auf gewisse Regeln oder Vorgaben achten zu müssen. Neben einigen Vorteilen zeigen Folksonomies aber auch zahlreiche Schwächen (u. a. einen Mangel an Präzision). Um diesen Nachteilen größtenteils entgegenzuwirken, schlagen wir eine Interpretation der Tags als natürlichsprachige Wörter vor. Dadurch ist es uns möglich, Methoden des Natural Language Processing (NLP) auf die Tags anzuwenden und so linguistische Probleme der Tags zu beseitigen. Darüber hinaus diskutieren wir Ansätze und weitere Vorschläge (Tagverteilungen, Kollaboration und akteurspezifische Aspekte) hinsichtlich eines Relevance Rankings von getaggten Dokumenten. Neben Vorschlägen auf ähnliche Dokumente (glqqmore like this!grqq) erlauben Folksonomies auch Hinweise auf verwandte Nutzer und damit auf Communities (glqqmore like me!grqq). Folksonomies in Knowledge Representation and Information Retrieval In Web 2.0 services grqqprosumers” -- producers and consumers -- collaborate not only for the purpose of creating content, but to index these pieces of information as well. Folksonomies permit actors to describe documents with subject headings, grqqtagsgrqq, without regarding any rules. Apart from a lot of benefits folksonomies have many shortcomings (e.g., lack of precision). In order to solve some of the problems we propose interpreting tags as natural language terms. Accordingly, we can introduce methods of NLP to solve the tags’ linguistic problems. Additionally, we present criteria for tagged documents to create a ranking by relevance (tag distribution, collaboration and actor-based aspects). Besides recommending similar documents (glqqmore like this!grqq) folksonomies can be used for the recommendation of similar users and communities (glqqmore like me!grqq). } }

%0 = article %A = Peters, Isabella and Stock, Wolfgang G. %D = 2008 %T = Folksonomies in Wissensrepräsentation und Information Retrieval %U = http://www.phil-fak.uni-duesseldorf.de/infowiss/admin/public_dateien/files/1/1204547968stock212_h.htm