@article{liu2012fulltext,
  author = {Liu, Xiaozhong and Zhang, Jinsong and Guo, Chun},
  interhash = {011df26355ad51a88947017fd2791a98},
  intrahash = {f9c6133bf4503003822f99860f864698},
  journal = {Journal of the American Society for Information Science and Technology},
  title = {Full-Text Citation Analysis: A New Method to Enhance Scholarly Network},
  url = {http://discern.uits.iu.edu:8790/publication/Full%20text%20citation.pdf},
  year = 2012
}

@article{Zhang20125759,
  abstract = {Social tagging is one of the most important ways to organize and index online resources. Recommendation in social tagging systems, e.g. tag recommendation, item recommendation and user recommendation, is used to improve the quality of tags and to ease the tagging or searching process. Existing works usually provide recommendations by analyzing relation information in social tagging systems, suffering a lot from the over sparse problem. These approaches ignore information contained in the content of resources, which we believe should be considered to improve recommendation quality and to deal with the over sparse problem. In this paper we propose a recommendation approach for social tagging systems that combines content and relation analysis in a single model. By modeling the generating process of social tagging systems in a latent Dirichlet allocation approach, we build a fully generative model for social tagging, leverage it to estimate the relation between users, tags and resources and achieve tag, item and user recommendation tasks. The model is evaluated using a CiteULike data snapshot, and results show improvements in metrics for various recommendation tasks.},
  author = {Zhang, Yin and Zhang, Bin and Gao, Kening and Guo, Pengwei and Sun, Daming},
  doi = {10.1016/j.physa.2012.05.013},
  interhash = {088ad59c786579d399aaee48db5e6a7a},
  intrahash = {84f824839090a5e20394b85a9e1cef08},
  issn = {0378-4371},
  journal = {Physica A: Statistical Mechanics and its Applications},
  number = 22,
  pages = {5759 - 5768},
  title = {Combining content and relation analysis for recommendation in social tagging systems},
  url = {http://www.sciencedirect.com/science/article/pii/S0378437112003846},
  volume = 391,
  year = 2012
}

@techreport{ilprints750,
  abstract = {The original PageRank algorithm for improving the ranking of search-query results computes a single vector, using the link structure of the Web, to capture the relative ``importance'' of Web pages, independent of any particular search query. To yield more accurate search results, we propose computing a {\em set} of PageRank vectors, biased using a set of representative topics, to capture more accurately the notion of importance with respect to a particular topic.  For ordinary keyword search queries, we compute the topic-sensitive PageRank scores for pages satisfying the query using the topic of the query keywords.  For searches done in context (e.g., when the search query is performed by highlighting words in a Web page), we compute the topic-sensitive PageRank scores using the topic of the context in which the query appeared.  By using linear combinations of these (precomputed) biased PageRank vectors to generate context-specific importance scores for pages at query time, we show that we can generate more accurate rankings than with a single, generic PageRank vector. },
  author = {Haveliwala, Taher H.},
  institution = {Stanford InfoLab},
  interhash = {959ab9af6c35acb5d8513fa032620ba7},
  intrahash = {34aedd24fc7a45f189be1ca70dfd99e2},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  note = {Extended version of the WWW2002 paper on Topic-Sensitive PageRank.},
  number = {2003-29},
  publisher = {Stanford InfoLab},
  title = {Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search},
  type = {Technical Report},
  url = {http://ilpubs.stanford.edu:8090/750/},
  year = 2003
}

@article{1742-5468-2007-06-P06010,
  abstract = {To account for strong ageing characteristics of citation networks, we modify the PageRank algorithm by initially distributing random surfers exponentially with age, in favour of more recent publications. The output of this algorithm, which we call CiteRank, is interpreted as approximate traffic to individual publications in a simple model of how researchers find new information. We optimize parameters of our algorithm to achieve the best performance. The results are compared for two rather different citation networks: all American Physical Society publications between 1893 and 2003 and the set of high-energy physics theory (hep-th) preprints. Despite major differences between these two networks, we find that their optimal parameters for the CiteRank algorithm are remarkably similar. The advantages and performance of CiteRank over more conventional methods of ranking publications are discussed.},
  author = {Walker, Dylan and Xie, Huafeng and Yan, Koon-Kiu and Maslov, Sergei},
  interhash = {86853f761733eaea09a273027a6c3c4a},
  intrahash = {ed618f45800255b5a5179d36849cd0b4},
  journal = {Journal of Statistical Mechanics: Theory and Experiment},
  number = 06,
  pages = {P06010},
  title = {Ranking scientific publications using a model of network traffic},
  url = {http://stacks.iop.org/1742-5468/2007/i=06/a=P06010},
  volume = 2007,
  year = 2007
}

@book{metzler2011featurecentric,
  asin = {3642228976},
  author = {Metzler, Donald},
  dewey = {005},
  ean = {9783642228971},
  edition = 2012,
  interhash = {4e473a9657c556434612d006a5a21460},
  intrahash = {22e5fe8501844167b64a5aed595f4372},
  isbn = {3642228976},
  publisher = {Springer},
  title = {A Feature-Centric View of Information Retrieval},
  url = {http://www.amazon.com/Feature-Centric-View-Information-Retrieval/dp/3642228976},
  year = 2011
}

@article{1117458,
  abstract = {Event-based network data consists of sets of events over time, each of which may involve multiple entities. Examples include email traffic, telephone calls, and research publications (interpreted as co-authorship events). Traditional network analysis techniques, such as social network models, often aggregate the relational information from each event into a single static network. In contrast, in this paper we focus on the temporal nature of such data. In particular, we look at the problems of temporal link prediction and node ranking, and describe new methods that illustrate opportunities for data mining and machine learning techniques in this context. Experimental results are discussed for a large set of co-authorship events measured over multiple years, and a large corporate email data set spanning 21 months.},
  address = {New York, NY, USA},
  author = {O'Madadhain, Joshua and Hutchins, Jon and Smyth, Padhraic},
  doi = {10.1145/1117454.1117458},
  interhash = {97a718ab9fe24625f7389939d2608d31},
  intrahash = {89a23b31a476c4f3f771b5e3e4a8432c},
  issn = {1931-0145},
  journal = {SIGKDD Explor. Newsl.},
  number = 2,
  pages = {23--30},
  publisher = {ACM},
  title = {Prediction and ranking algorithms for event-based network data},
  url = {http://portal.acm.org/citation.cfm?id=1117458},
  volume = 7,
  year = 2005
}

@inproceedings{conf/ht/WuZM06,
  author = {Wu, Harris and Zubair, Mohammad and Maly, Kurt},
  booktitle = {Hypertext},
  crossref = {conf/ht/2006},
  date = {2006-09-28},
  editor = {Wiil, Uffe Kock and Nürnberg, Peter J. and Rubart, Jessica},
  ee = {http://doi.acm.org/10.1145/1149941.1149962},
  interhash = {ea6aa5db3724812d08347d5a8309bea4},
  intrahash = {4b0512091911843390f88699d3ea3bb9},
  isbn = {1-59593-417-0},
  pages = {111-114},
  publisher = {ACM},
  title = {Harvesting social knowledge from folksonomies.},
  url = {http://dblp.uni-trier.de/db/conf/ht/ht2006.html#WuZM06},
  year = 2006
}

@inproceedings{abel2009contextbased,
  abstract = {With the advent of Web 2.0 tagging became a popular feature. People tag diverse kinds of content, e.g. products at Amazon, music at Last.fm, images at Flickr, etc. Clicking on a tag enables the users to explore related content. In this paper we investigate how such tag-based queries, initialized by the clicking activity, can be enhanced with automatically produced contextual information so that the search result better fits to the actual aims of the user. We introduce the SocialHITS algorithm and present an experiment where we compare different algorithms for ranking 
users, tags, and resources in a contextualized way.},
  address = {New York, NY, USA},
  author = {Abel, Fabian and Baldoni, Matteo and Baroglio, Cristina and Henze, Nicola and Krause, Daniel and Patti, Viviana},
  booktitle = {HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia},
  interhash = {0e0dff0c21fd77d2d1f0224317c4974f},
  intrahash = {17d5c35426963e20875ec1dc42913855},
  month = {July},
  paperid = {fp060},
  publisher = {ACM},
  session = {Full Paper},
  title = {Context-based Ranking in Folksonomies},
  year = 2009
}

@inproceedings{conf/grc/ZhangZ05b,
  author = {Zhang, Min-Ling and Zhou, Zhi-Hua},
  booktitle = {GrC},
  crossref = {conf/grc/2005},
  date = {2007-03-22},
  editor = {Hu, Xiaohua and Liu, Qing and Skowron, Andrzej and Lin, Tsau Young and Yager, Ronald R. and Zhang, Bo},
  ee = {http://doi.ieeecomputersociety.org/10.1109/GRC.2005.1547385},
  interhash = {3c58a80457442249a999a2ceea877565},
  intrahash = {3460603745790c4824a9bdb572e64777},
  isbn = {0-7803-9017-2},
  pages = {718-721},
  publisher = {IEEE},
  title = {A k-nearest neighbor based algorithm for multi-label classification.},
  url = {http://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/grc05.pdf},
  year = 2005
}

@inproceedings{1458233,
  address = {New York, NY, USA},
  author = {Chen, Keke and Lu, Rongqing and Wong, C. K. and Sun, Gordon and Heck, Larry and Tseng, Belle},
  booktitle = {CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge management},
  doi = {http://doi.acm.org/10.1145/1458082.1458233},
  interhash = {ab045aa8016700e8a7c93f5c55dc91fe},
  intrahash = {0e3e57ca0edda99c53dc3101ffeaef96},
  isbn = {978-1-59593-991-3},
  location = {Napa Valley, California, USA},
  pages = {1143--1152},
  publisher = {ACM},
  title = {Trada: tree based ranking function adaptation},
  url = {http://portal.acm.org/citation.cfm?doid=1458082.1458233},
  year = 2008
}

@misc{Maslov2009,
  abstract = {  We review our recent work on applying the Google PageRank algorithm to find scientific "gems" among all Physical Review publications, and its extension to CiteRank, to find currently popular research directions. These metrics provide a meaningful extension to traditionally-used importance measures, such as the number of citations and journal impact factor. We also point out some pitfalls of over-relying on quantitative metrics to evaluate scientific quality. },
  author = {Maslov, Sergei and Redner, S.},
  interhash = {8f0a3a222a5c357e4db423ec065065da},
  intrahash = {d2b34ecaa23078ebef7a7ee84be509a4},
  note = {cite arxiv:0901.2640 Comment: 3 pages, 1 figure, invited comment for the Journal of Neuroscience.   The arxiv version is microscopically different from the published version},
  title = {Promise and Pitfalls of Extending Google's PageRank Algorithm to   Citation Networks},
  url = {http://arxiv.org/abs/0901.2640},
  year = 2009
}

@misc{ailon-2007,
  abstract = { This paper describes an efficient reduction of the learning problem of ranking to binary classification. The reduction guarantees an average pairwise misranking regret of at most that of the binary classifier regret, improving a recent result of Balcan et al which only guarantees a factor of 2. Moreover, our reduction applies to a broader class of ranking loss functions, admits a simpler proof, and the expected running time complexity of our algorithm in terms of number of calls to a classifier or preference function is improved from $\Omega(n^2)$ to $O(n \log n)$. In addition, when the top $k$ ranked elements only are required ($k \ll n$), as in many applications in information extraction or search engines, the time complexity of our algorithm can be further reduced to $O(k \log k + n)$. Our reduction and algorithm are thus practical for realistic applications where the number of points to rank exceeds several thousands. Much of our results also extend beyond the bipartite case previously studied.},
  author = {Ailon, Nir and Mohri, Mehryar},
  interhash = {b102fea8a5381448d5b624aa2b82bc50},
  intrahash = {d8bd1b99e3c245d17b577514727ebff2},
  title = {An efficient reduction of ranking to classification},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:0710.2889},
  year = 2007
}

@inproceedings{AlKhalifa:2007,
  abstract = {Users tag resources for a variety of reasons and using a variety of conventions. The tags that they provide are stored in social bookmarking services, so these services can provide a rich gateway to a wide and interesting quantity of web resources. The cognitive effort that has gone into making these tags has presumably added value to the description of the resource. In this work we utilize the quantitative value of these tags for ranking bookmarked web resources in social bookmarking services. Our proposed solution is called CoolRank, a simple and intuitive model to rank bookmarked web resources in a social bookmarking service, such as del.icio.us. CoolRank makes use of both quantitative information, based on the number of people who have bookmarked a web resource, and subjective information, based on the words people have used in their tags.},
  author = {Al-Khalifa, H.S.},
  booktitle = {Innovations in Information Technology, 2007. Innovations '07. 4th International Conference on},
  doi = {10.1109/IIT.2007.4430482},
  interhash = {a6babb1a2f926cca3e8fe0258337e864},
  intrahash = {4671fb1c606e3d7f559bb25d9b20e47d},
  isbn = {978-1-4244-1841-1},
  pages = {208-212},
  title = {CoolRank: A Social Solution for Ranking Bookmarked Web Resources},
  url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4430482},
  year = 2007
}

@article{Butler:2008:Nature:18172465,
  author = {Butler, D},
  doi = {10.1038/451006a},
  interhash = {d4652ec77b4ae09062fac4676cea6bb7},
  intrahash = {d930c99c8e9c7fdf8ded3e9edb0762b0},
  journal = {Nature},
  month = Jan,
  number = 7174,
  pages = {6-6},
  pmid = {18172465},
  title = {Free journal-ranking tool enters citation market},
  url = {http://www.nature.com/news/2008/080102/full/451006a.html},
  volume = 451,
  year = 2008
}

@article{10.1109/WI.2007.108,
  address = {Los Alamitos, CA, USA},
  author = {Nauman, Mohammad and Khan, Shahbaz},
  doi = {http://doi.ieeecomputersociety.org/10.1109/WI.2007.108},
  interhash = {ed3957694fe4ccb1137780c720b7d79a},
  intrahash = {799817443dab31b534315a790c24a9f6},
  isbn = {0-7695-3026-5},
  journal = {wi},
  pages = {423-426},
  publisher = {IEEE Computer Society},
  title = {Using PersonalizedWeb Search for Enhancing Common Sense and Folksonomy Based Intelligent Search Systems},
  volume = 0,
  year = 2007
}

@article{sinclair:ftc,
  author = {Sinclair, J. and Cardew-Hall, M.},
  interhash = {fe7fb4aad79ca5ee3ba8a5b2e1c3cd5b},
  intrahash = {539fe40eb8dd2597956cae27d6fb02ac},
  journal = {Journal of Information Science},
  pages = 016555150607808,
  publisher = {CILIP},
  title = {{The folksonomy tag cloud: When is it useful?}},
  year = 2007
}

@inproceedings{hotho2006information,
  address = {Budva, Montenegro},
  author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {Proceedings of the 3rd European Semantic Web Conference },
  interhash = {10ec64d80b0ac085328a953bb494fb89},
  intrahash = {7da1127fc4836e2cf58e3073f1b888b2},
  isbn = {3-540-34544-2},
  month = {June},
  pages = {411-426},
  publisher = {Springer},
  series = {LNCS},
  title = {Information Retrieval in Folksonomies: Search and Ranking},
  url = {http://www.kde.cs.uni-kassel.de/hotho/pub/2006/seach2006hotho_eswc.pdf},
  vgwort = {29},
  volume = 4011,
  year = 2006
}

@article{kleinberg1999hits,
  abstract = {. The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have effective means for understanding it. We develop a set of algorithmic tools for extracting information from the link structures of such environments, and report on experiments that demonstrate their effectiveness in a variety of contexts on the World Wide Web. The central issue we address within our framework is the distillation of broad search topics,...},
  author = {Kleinberg, Jon M.},
  citeulike-article-id = {1115},
  comment = {HITS algorithm},
  interhash = {48a48add3cba613f07df1e9b56278b85},
  intrahash = {c86549355475331f563d0a3ba7816dab},
  journal = {Journal of the ACM},
  number = 5,
  pages = {604--632},
  priority = {1},
  title = {Authoritative sources in a hyperlinked environment},
  url = {http://citeseer.ist.psu.edu/kleinberg99authoritative.html},
  volume = 46,
  year = 1999
}

@inproceedings{Pageetal98,
  address = {Brisbane, Australia},
  author = {Page, L. and Brin, S. and Motwani, R. and Winograd, T.},
  booktitle = {Proceedings of the 7th International World Wide Web Conference},
  interhash = {ca10cf0b0dd668c64b1f378ff0775849},
  intrahash = {ac49c33e114ca171db40cece6a0ae4d6},
  pages = {161--172},
  title = {The PageRank citation ranking: Bringing order to the Web},
  url = {citeseer.nj.nec.com/page98pagerank.html},
  year = 1998
}

@inproceedings{breese98empirical,
  author = {Breese, John S. and Heckerman, David and Kadie, Carl},
  booktitle = {Proceedings of the 14$^{th}$ Conference on Uncertainty in Artificial Intelligence},
  interhash = {593f72dfa20e4b7b5b16205479989020},
  intrahash = {82cd7b6c312f4181b1d05adb10c1d56a},
  pages = {43-52},
  title = {Empirical Analysis of Predictive Algorithms for Collaborative Filtering},
  year = 1998
}

@inproceedings{freyne07,
  address = {New York, NY, USA},
  author = {Freyne, Jill and Farzan, Rosta and Brusilovsky, Peter and Smyth, Barry and Coyle, Maurice},
  booktitle = {IUI '07: Proceedings of the 12th international conference on Intelligent user interfaces},
  doi = {http://doi.acm.org/10.1145/1216295.1216312},
  interhash = {871e012dc7b1c131d32480f1e3a655e7},
  intrahash = {88603ee0903b30dc642aebdaa6a22f93},
  isbn = {1-59593-481-2},
  location = {Honolulu, Hawaii, USA},
  pages = {52--61},
  publisher = {ACM Press},
  title = {Collecting community wisdom: integrating social search \& social navigation},
  url = {http://portal.acm.org/citation.cfm?id=1216312},
  year = 2007
}

@unpublished{szekely2006ranking,
  author = {Szekely, Benjamin and Torres, Elias},
  interhash = {3c31525eac065856391242454cdcf7a6},
  intrahash = {2d307d46e596d58844014895928051dc},
  month = may,
  title = {Ranking Bookmarks and Bistros: Intelligent Community and Folksonomy Development},
  url = {http://torrez.us/archives/2005/07/13/tagrank.pdf},
  year = 2005
}

@misc{michail2005collaborativerank,
  author = {Michail, Amir},
  day = 23,
  howpublished = {\url{http://collabrank.web.cse.unsw.edu.au/collabrank.pdf}},
  institution = {School of Computer Science and Engineering},
  interhash = {792c8f9b881af577e8e7e0d488562951},
  intrahash = {876a732f49e2a5e44b0df3fe5c6fe2a1},
  month = {April},
  note = {(work in progress)},
  title = {{CollaborativeRank: Motivating People to Give Helpful and Timely Ranking Suggestions}},
  url = {\url{http://collabrank.web.cse.unsw.edu.au/collabrank.pdf}},
  year = 2005
}

@techreport{Vojnovic,
  author = {Vojnovic, M. and Cruise, J. and Gunawardena, D. and Marbach, P.},
  date = {Feb 2007},
  howpublished = {MSR-TR-2007-06},
  interhash = {687129a4106fab9a2cd1c032ae52fc73},
  intrahash = {1af9877eebe38cdedc6578967ae76d8a},
  tech = {TR-2007-06},
  title = {Ranking and Suggesting Tags in Collaborative Tagging Applications},
  url = {http://research.microsoft.com/~milanv/tagbooster.aspx},
  year = 2007
}

@inproceedings{hotho2006folkrank,
  abstract = { In social bookmark tools users are setting up
lightweight conceptual structures called folksonomies. Currently,
the information retrieval support is limited. We present a formal
model and a new search algorithm for folksonomies, called
FolkRank, that exploits the structure of the folksonomy. The
proposed algorithm is also applied to find communities within the
folksonomy and is used to structure search results. All findings are
demonstrated on a large scale dataset. A long version of this paper
has been published at the European Semantic Web Conference
2006.},
  author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {Proc. FGIR 2006},
  interhash = {3468dc3fed17eadf2e7c6ff06fbb34a3},
  intrahash = {4d8b4f79814691fbe6db8357d63206a1},
  issn = {0941-3014},
  pages = {111-114},
  title = {FolkRank: A Ranking Algorithm for Folksonomies},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006folkrank.pdf},
  vgwort = {8},
  year = 2006
}

@inproceedings{DBLP:conf/semweb/DingPFJPK05,
  author = {Ding, Li and Pan, Rong and Finin, Timothy W. and Joshi, Anupam and Peng, Yun and Kolari, Pranam},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  booktitle = {International Semantic Web Conference},
  crossref = {DBLP:conf/semweb/2005},
  ee = {http://dx.doi.org/10.1007/11574620_14},
  interhash = {fbd1c77889dc928ae2c157761d1b5567},
  intrahash = {12ca34d21f87f455fe38ca90c5ddf377},
  pages = {156-170},
  title = {Finding and Ranking Knowledge on the Semantic Web.},
  url = {http://ebiquity.umbc.edu/get/a/publication/197.pdf},
  year = 2005
}