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Automatically Identifying Tag Types [chapter]

Kerstin Bischoff, Claudiu S. Firan, Cristina Kadar, Wolfgang Nejdl, Raluca Paiu
2009 Lecture Notes in Computer Science  
We based our decision on the most popular resource(s) tagged.  ... 
doi:10.1007/978-3-642-03348-3_7 fatcat:kt3higgvcndfrnfyldeh2decpy

Music Mood And Theme Classification - A Hybrid Approach

Kerstin Bischoff, Claudiu S. Firan, Raluca Paiu, Wolfgang Nejdl, Cyril Laurier, Mohamed Sordo
2009 Zenodo  
total -set of songs 1: For each song si ∈ S total Compute Pa(si) = {p SV M (mj |si)} = {pa(mj |si)} and Pt(si) = {p N B (mj |si)} = {pt(mj |si)} (see Alg. 1, step 4) 2: For each α=0.1,...,0.9, step=0.1  ...  at least X songs Discard class 3: Classifier learns a model 3a: Split song set S total into Strain = songs used for training the classifier Stest = songs used for testing the classifiers' learned model  ... 
doi:10.5281/zenodo.1417317 fatcat:pnh4uogd6rbhngzs7eenyknnhm

PHAROS - Personalizing Users' Experience in Audio-Visual Online Spaces

Raluca Paiu, Ling Chen, Claudiu S. Firan, Wolfgang Nejdl
2008 PersDB / PersDL  
User profiles are constructed initially from the personal data a user enters when s/he creates his/her profile. UCP adds more data to the profile as the user starts using the platform.  ... 
dblp:conf/persdb/PaiuCFN08 fatcat:tv2ijecmlfez3ipjmraxwgu6ou

Ranking Entities Using Web Search Query Logs [chapter]

Bodo Billerbeck, Gianluca Demartini, Claudiu S. Firan, Tereza Iofciu, Ralf Krestel
2010 Lecture Notes in Computer Science  
We label these approaches as U C,S e.g. U C2,S1 in the case of the union of C 2 and S 1 . Intersection.  ...  Level 2 query scores are then computed as S query (q j ) = i S url (u i ) where u i are all the clicked URLs for query q j .  ... 
doi:10.1007/978-3-642-15464-5_28 fatcat:awqjas7azrcovanarwwlim36zq

Why finding entities in Wikipedia is difficult, sometimes

Gianluca Demartini, Claudiu S. Firan, Tereza Iofciu, Ralf Krestel, Wolfgang Nejdl
2010 Information retrieval (Boston)  
doi:10.1007/s10791-010-9135-7 fatcat:7vqvszi72rg4def5d3sqcupy3i

Deriving music theme annotations from user tags

Kerstin Bischoff, Claudiu S. Firan, Raluca Paiu
2009 Proceedings of the 18th international conference on World wide web - WWW '09  
Music theme annotations would be really beneficial for supporting retrieval, but are often neglected by users while annotating. Thus, in order to support users in tagging and to fill the gaps in the tag space, in this paper we develop algorithms for recommending theme annotations. Our methods exploit already existing user tags, the lyrics of music tracks, as well as combinations of both. We compare the results for our recommended theme annotations against genre and style recommendations -a much
more » ... easier and already studied task. We evaluate the quality of our recommended tags against an expert ground truth data set. Our results are promising and provide interesting insights into possible extensions for music tagging systems to support music search.
doi:10.1145/1526709.1526924 dblp:conf/www/BischoffFP09 fatcat:sa4yi33canfsbmils4hbqmqn3u

GLOCAL

Pierre Andrews, Vanessa Murdock, Adam Rae, Francesco De Natale, Sven Buschbeck, Anthony Jameson, Kerstin Bischoff, Claudiu S. Firan, Claudia Niederée, Vasileios Mezaris, Spiros Nikolopoulos
2012 Proceedings of the 21st international conference companion on World Wide Web - WWW '12 Companion  
The idea of the European project GLOCAL is to use events as the central concept for search, organization and combination of multimedia content from various sources. For this purpose methods for event detection and event matching as well as media analysis are developed. Considered events range from private, over local, to global events. Figure 2: (A) A filtered and partly collapsed representation of the 2010 soccer World Cup as a hierarchy of events); (B) The user has zoomed in on a single game
more » ... nd clicked on the "media" links for two goals, so as to be able to compare the associated media. WWW 2012 -European Projects Track
doi:10.1145/2187980.2188013 dblp:conf/www/AndrewsNBJBFNMNMR12 fatcat:zhksq5dnorexvmf5ynpd4ez4iq

Can all tags be used for search?

Kerstin Bischoff, Claudiu S. Firan, Wolfgang Nejdl, Raluca Paiu
2008 Proceeding of the 17th ACM conference on Information and knowledge mining - CIKM '08  
We based our decision on the most popular resource(s) tagged.  ... 
doi:10.1145/1458082.1458112 dblp:conf/cikm/BischoffFNP08 fatcat:6zw45zpzh5aivc5jcex77zwdsy

Summarizing local context to personalize global web search

Paul-Alexandru Chirita, Claudiu S. Firan, Wolfgang Nejdl
2006 Proceedings of the 15th ACM international conference on Information and knowledge management - CIKM '06  
40] , if N S > 40 with N S being the total number of sentences in the document.  ...  A word is significant in a document if its real frequency (i.e., not logged) is above a threshold as follows: T F > ms = 8 < : 7 − 0.1 * [25 − N S] , if N S < 25 7 , if N S ∈ [25, 40] 7 + 0.1 * [N S −  ... 
doi:10.1145/1183614.1183658 dblp:conf/cikm/ChiritaFN06 fatcat:6jhufvqwwrdfhgen2boad5rryi

Personalized query expansion for the web

Paul - Alexandru Chirita, Claudiu S. Firan, Wolfgang Nejdl
2007 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '07  
A word is significant in a document if its frequency is above a threshold as follows: T F > ms = V X 7 − 0.1 * (25 − N S) , if N S < 25 7 , if N S ∈ [25, 40] 7 + 0.1 * (N S − 40) , if N S > 40 with N S  ...  The second term is a position score set to (Avg(N S) − SentenceIndex)/Avg 2 (N S) for the first ten sentences, and to 0 otherwise, Avg(N S) being the average number of sentences over all Desktop items.  ... 
doi:10.1145/1277741.1277746 dblp:conf/sigir/ChiritaFN07 fatcat:b2ff5blcmrefpfo6pfdlbojnsa

Pushing task relevant web links down to the desktop

Paul - Alexandru Chirita, Claudiu S. Firan, Wolfgang Nejdl
2006 Proceedings of the eighth ACM international workshop on Web information and data management - WIDM '06  
7 + 0.1 · (N S − 40) , if N S > 40 with N S being the total number of sentences in the document.  ...  A word is significant in a document if its real frequency (i.e., not the log of the frequency) is above a threshold as follows: T F > ms = 8 < : 7 − 0.1 · (25 − N S) , if N S < 25 7 , if N S ∈ [25, 40]  ... 
doi:10.1145/1183550.1183563 dblp:conf/widm/ChiritaFN06 fatcat:fmbqq3rzyffbdo5eo4svh2cooe

Lexical analysis for modeling web query reformulation

Alessandro Bozzon, Paul - Alexandru Chirita, Claudiu S. Firan, Wolfgang Nejdl
2007 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '07  
The major transition patterns from these queries involve either adding even more words, or removing the adjective(s).  ...  We performed our empirical investigation onto an Excite log of about 2.4 million queries sent over 8Copyright is held by the author/owner(s). SIGIR'07, July 23-27, 2007, Amsterdam, The Netherlands.  ... 
doi:10.1145/1277741.1277885 dblp:conf/sigir/BozzonCFN07 fatcat:b5nseulqu5cslm6kc3n72rnyyu

Bridging the Gap—Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0

Bettina Berendt, Andreas Hotho, Gerd Stumme
2010 Journal of Web Semantics  
The first paper, Bridging the Gap Between Tagging and Querying Vocabularies: Analyses and Applications for Enhancing Multimedia IR by Kerstin Bischoff, Claudiu Firan, Wolfgang Nejdl and Raluca Paiu, is  ...  The authors observe that “HowTo”s on the Web are a rich source of data and knowledge, but are barely semantically structured today, which makes search for previous successful solutions to a problem like  ... 
doi:10.1016/j.websem.2010.04.008 fatcat:kaq3tbp52bhv3mltismz4rgxwm

Exploiting click-through data for entity retrieval

Bodo Billerbeck, Gianluca Demartini, Claudiu Firan, Tereza Iofciu, Ralf Krestel
2010 Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '10  
Copyright is held by the author/owner(s). SIGIR'10, July 19-23, 2010, Geneva, Switzerland. ACM 978-1-60558-896-4/10/07.  ... 
doi:10.1145/1835449.1835624 dblp:conf/sigir/BillerbeckDFIK10 fatcat:nqurnurrnnfazpqhbitw7cdpkm

GENRE PREDICTION FOR MUSIC RECOMMENDATION USING MACHINE LEARNING

Arpit Seth
2020 EPRA international journal of research & development  
Claudiu, W. N. Firan and P. Raluca [31] proposed a system in which users are collaboratively grouped together based on users rating and profile.  ...  Music is one of the vast spread industry shortlisting music tracks according to user"s preference is difficult.  ... 
doi:10.36713/epra4283 fatcat:oczykynz25fwnccuecpdqro5ji
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