@inproceedings{halpin2006dynamics,
        title = {The Dynamics and Semantics of Collaborative Tagging },
        author = {Harry Halpin and Valentin Robu and Hana Shepard},
        booktitle = {Proceedings of the 1st Semantic Authoring and Annotation Workshop (SAAW'06)},
        year = 2006,
        url = {http://sunsite.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-209/saaw06-full01-halpin.pdf},
        abstract = {The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including the dynamics of such systems and whether coherent classification schemes can emerge from undirected tagging by users. Currently millions of users are using collaborative tagging without centrally organizing principles, and many suspect this exhibits features considered to be indicative of a complex system. If this is the case, it remains to be seem whether collaborative tagging by users over time leads to emergent classi- fication schemes that could be formalized into an ontology usable by the Semantic Web. This paper uses data from “popular” tagged sites on the social bookmarking site del.icio.us to examine the dynamics of such collaborative tagging systems. In particular, we are trying to determine whether the distribution of tag frequencies stabilizes, which indicates a degree of cohesion or consensus among users about the optimal tags to describe particular sites. We use tag co-occurrence networks for a sample domain of tags to analyze the meaning of particular tags given their relationship to other tags and automatically create an ontology. We also produce a generative model of collaborative tagging in order to model and understand some of the basic dynamics behind the process.},
	biburl = {http://www.bibsonomy.org/bibtex/27be112719b93a4d4263407afbf05cce1/wnpxrz},
	keywords = {tagging collaborative filtering}
}

@article{herlocker2004evaluating,
        title = {Evaluating collaborative filtering recommender systems},
        address = {New York, NY, USA},
        author = {Jonathan L. Herlocker and Joseph A. Konstan and Loren G. Terveen and John T. Riedl},
        journal = {ACM Trans. Inf. Syst.},
        number = 1,
        pages = {5--53},
        publisher = {ACM Press},
        volume = 22,
        year = 2004,
        url = {http://portal.acm.org/citation.cfm?id=963770.963772},
        issn = {1046-8188},
	doi = {http://doi.acm.org/10.1145/963770.963772},
	description = {Evaluating collaborative filtering recommender systems},
	abstract = {Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.},
	biburl = {http://www.bibsonomy.org/bibtex/2bdd3980bb3c297d1b84ceb0c7729d397/wnpxrz},
	keywords = {recommendersystems collaborative evaluation filtering}
}

@article{keyhere,
        title = {Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering},
        author = {Zan Huang and Hsinchun Chen and Daniel Zeng},
        year = 2004,
        url = {http://dlist.sir.arizona.edu/429/},
        typesource = {Simple CitationSource},
	source = {},
	asin = {},
	pubmed = {},
	doi = {},
	description = {DLIST - Applying Associative Retrieval Techniques to Alleviate the Sparsity Problem in Collaborative Filtering},
	biburl = {http://www.bibsonomy.org/bibtex/2d32c3bfdeb71734dd020d29f9ccae7d6/wnpxrz},
	keywords = {filtering imported collaborative}
}

@misc{melville01contentboosted,
        title = {Content-boosted collaborative filtering},
        author = {P. Melville and R. Mooney and R. Nagarajan},
        year = 2001,
        url = {citeseer.ist.psu.edu/melville01contentboosted.html},
        text = {In Proceedings of the 2001.},
	description = {Content-Boosted Collaborative Filtering - Melville, Mooney, Nagarajan (ResearchIndex)},
	biburl = {http://www.bibsonomy.org/bibtex/275a1b0eda56eb490f1ae43f9887e1605/wnpxrz},
	keywords = {filtering imported collaborative}
}

@misc{connor01clustering,
        title = {Clustering items for collaborative filtering},
        author = {M. Connor and J. Herlocker},
        year = 2001,
        url = {citeseer.ist.psu.edu/connor01clustering.html},
        description = {Clustering Items for Collaborative Filtering - Connor, Herlocker (ResearchIndex)},
	biburl = {http://www.bibsonomy.org/bibtex/2c0928a69f114f03cc84de845b044c39d/wnpxrz},
	keywords = {collaborative filtering imported}
}

@inproceedings{223929,
        title = {Recommending and evaluating choices in a virtual community of use},
        address = {New York, NY, USA},
        author = {Will Hill and Larry Stead and Mark Rosenstein and George Furnas},
        booktitle = {CHI '95: Proceedings of the SIGCHI conference on Human factors in computing systems},
        pages = {194--201},
        publisher = {ACM Press/Addison-Wesley Publishing Co.},
        year = 1995,
        url = {http://portal.acm.org/citation.cfm?id=223929&dl=GUIDE&coll=GUIDE&CFID=49137213&CFTOKEN=98745588},
        location = {Denver, Colorado, United States},
	isbn = {0-201-84705-1},
	doi = {http://doi.acm.org/10.1145/223904.223929},
	description = {Recommending and evaluating choices in a virtual community of use},
	biburl = {http://www.bibsonomy.org/bibtex/2e111f8272ac836796cd546765cd86d0f/wnpxrz},
	keywords = {collaborative filtering imported}
}

@article{138867,
        title = {Using collaborative filtering to weave an information tapestry},
        address = {New York, NY, USA},
        author = {David Goldberg and David Nichols and Brian M. Oki and Douglas Terry},
        journal = {Commun. ACM},
        number = 12,
        pages = {61--70},
        publisher = {ACM},
        volume = 35,
        year = 1992,
        url = {http://portal.acm.org/citation.cfm?id=138867&dl=GUIDE&coll=GUIDE&CFID=49137213&CFTOKEN=98745588},
        issn = {0001-0782},
	doi = {http://doi.acm.org/10.1145/138859.138867},
	description = {Using collaborative filtering to weave an information tapestry},
	biburl = {http://www.bibsonomy.org/bibtex/2f4c5048e4b774037f64ca88ad0bcd8b5/wnpxrz},
	keywords = {filtering imported collaborative}
}

@article{245126,
        title = {GroupLens: applying collaborative filtering to Usenet news},
        address = {New York, NY, USA},
        author = {Joseph A. Konstan and Bradley N. Miller and David Maltz and Jonathan L. Herlocker and Lee R. Gordon and John Riedl},
        journal = {Commun. ACM},
        number = 3,
        pages = {77--87},
        publisher = {ACM},
        volume = 40,
        year = 1997,
        url = {http://portal.acm.org/citation.cfm?id=245108.245126},
        issn = {0001-0782},
	doi = {http://doi.acm.org/10.1145/245108.245126},
	description = {GroupLens},
	biburl = {http://www.bibsonomy.org/bibtex/2b53b2dfa6b9064d2ad77fd6440ca48b3/wnpxrz},
	keywords = {imported grouplens collaborative filtering}
}

@misc{hofmann-collaborative,
        title = {Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis},
        author = {Thomas Hofmann},
        year = 2003,
        url = {citeseer.ist.psu.edu/hofmann03collaborative.html},
        description = {Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis (ResearchIndex)},
	biburl = {http://www.bibsonomy.org/bibtex/2ee9a97fe4b415a84b145ca5b752a8c46/wnpxrz},
	keywords = {filtering collaborative gaussian lsa imported}
}

@misc{liu-2007,
        title = {A spreading activation approach for collaborative filtering},
        author = {Jian-Guo Liu},
        year = 2007,
        url = {http://www.citebase.org/abstract?id=oai:arXiv.org:0712.3807},
        description = {[0712.3807] A spreading activation approach for collaborative filtering},
	abstract = { In this Brief Report, we propose a spreading activation approach for collaborative filtering (SA-CF). Under the simplest case with binary resource, the current algorithm has remarkably higher accuracy than the standard collaborative filtering (CF) using Pearson correlation. Furthermore, we introduce a free parameter $\beta$ to regulate the contributions of objects to user-user correlations. The numerical results indicate that decreasing the influence of popular objects can further improve the algorithmic accuracy. We argue that a better algorithm should simultaneously require less computation and generate higher accuracy. Accordingly, we further propose an algorithm involving only the top-$N$ similar neighbors for each target user, which has both less computational complexity and higher algorithmic accuracy.},
	biburl = {http://www.bibsonomy.org/bibtex/2151260b54a8e92cbc4c940745ee6bdd8/wnpxrz},
	keywords = {filtering imported collaborative}
}

@misc{terveen01beyond,
        title = {Beyond Recommender Systems: Helping People Help Each Other},
        author = {L. Terveen and W. Hill},
        year = 2001,
        url = {citeseer.ist.psu.edu/terveen01beyond.html},
        description = {Beyond Recommender Systems: Helping People Help Each Other - Terveen, Hill (ResearchIndex)},
	biburl = {http://www.bibsonomy.org/bibtex/2e26b0d4305079fae673c8dcdb1999d68/wnpxrz},
	keywords = {collaborative imported recommendersystems filtering}
}

@inproceedings{begelman2006clustering,
        title = {Automated Tag Clustering:
Improving search and exploration in the tag space},
        author = {Grigory Begelman and Philipp Keller and Frank Smadja},
        booktitle = {Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland},
        year = 2006,
        id = {699842},
	priority = {3},
	biburl = {http://www.bibsonomy.org/bibtex/276b741061fab004645c3119db5a17bc3/wnpxrz},
	keywords = {folksonomy filtering tags tag exploration search collaborative clustering}
}

@proceedings{DBLP:conf/kdd/1999web,
        title = {Web Usage Analysis and User Profiling, International WEBKDD'99
               Workshop, San Diego, California, USA, August 15, 1999, Revised
               Papers},
        booktitle = {WEBKDD},
        editor = {Brij M. Masand and Myra Spiliopoulou},
        publisher = {Springer},
        series = {Lecture Notes in Computer Science},
        volume = 1836,
        year = 2000,
        bibsource = {DBLP, http://dblp.uni-trier.de},
	isbn = {3-540-67818-2},
	biburl = {http://www.bibsonomy.org/bibtex/218a9697e8ca04f637487e79b6be9cc83/wnpxrz},
	keywords = {web profile collaborative proj:bk proj:mh filtering profiling user}
}

@inproceedings{295795,
        title = {Recommendation as classification: using social and content-based information in recommendation},
        address = {Menlo Park, CA, USA},
        author = {Chumki Basu and Haym Hirsh and William Cohen},
        booktitle = {AAAI '98/IAAI '98: Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence},
        pages = {714--720},
        publisher = {American Association for Artificial Intelligence},
        year = 1998,
        url = {ftp://ftp.cs.rutgers.edu/pub/hirsh/papers/1998/aaai1.ps},
        location = {Madison, Wisconsin, United States},
	isbn = {0-262-51098-7},
	biburl = {http://www.bibsonomy.org/bibtex/290f4b7eab8a7a308c6e077a993cd19d8/wnpxrz},
	keywords = {classification filtering content recommendersystems social collaborative}
}

@inproceedings{657311,
        title = {Learning Collaborative Information Filters},
        address = {San Francisco, CA, USA},
        author = {Daniel Billsus and Michael J. Pazzani},
        booktitle = {ICML '98: Proceedings of the Fifteenth International Conference on Machine Learning},
        pages = {46--54},
        publisher = {Morgan Kaufmann Publishers Inc.},
        year = 1998,
        url = {http://www.ics.uci.edu/~pazzani/Publications/MLC98.pdf},
        isbn = {1-55860-556-8},
	description = {Learning Collaborative Information Filters},
	biburl = {http://www.bibsonomy.org/bibtex/2977851e8e6cb73b8b94b0cea69dbb9e3/wnpxrz},
	keywords = {filtering collaborative machinelearning}
}

@inproceedings{Niwa:2006,
        title = {Web Page Recommender System based on Folksonomy Mining for ITNG ’06 Submissions},
        author = {S. Niwa and Takuo Doi and S. Honiden},
        booktitle = {Information Technology: New Generations, 2006. ITNG 2006. Third International Conference on},
        pages = {388- 393},
        year = 2006,
        url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1611624},
        isbn = {0-7695-2497-4},
	doi = {10.1109/ITNG.2006.140},
	description = {Welcome to IEEE Xplore 2.0: Web Page Recommender System based on Folksonomy Mining for ITNG ’06 Submissions},
	abstract = {There have been many attempts to construct web page recommender systems using collaborative filtering. But the domains these systems can cover are very restricted because it is very difficult to assemble user preference data to web pages, and the number of web pages on the Internet is too large. In this paper, we propose the way to construct a new type of web page recommender system covering all over the Internet, by using Folksonomy and Social Bookmark which are getting very popular in these days.},
	biburl = {http://www.bibsonomy.org/bibtex/2b1cb4183d3ad183709ed11780f1b5fdf/wnpxrz},
	keywords = {filtering recommendersystems folksonomy tags collaborative imported web tagging}
}

@article{keyhere,
        title = {Cubic Analysis of Social Bookmarking for Personalized Recommendation},
        author = {Yanfei Xu and Liang Zhang and Wei Liu},
        journal = {Frontiers of WWW Research and Development - APWeb 2006},
        pages = {733--738},
        year = 2006,
        url = {http://dx.doi.org/10.1007/11610113_66},
        description = {SpringerLink - Book Chapter},
	abstract = {Personalized recommendation is used to conquer the information overload problem, and collaborative filtering recommendation (CF) is one of the most successful recommendation techniques to date. However, CF becomes less effective when users have multiple interests, because users have similar taste in one aspect may behave quite different in other aspects. Information got from social bookmarking websites not only tells what a user likes, but also why he or she likes it. This paper proposes a division algorithm and a CubeSVD algorithm to analysis this information, distill the interrelations between different users’ various interests, and make better personalized recommendation based on them. Experiment reveals the superiority of our method over traditional CF methods.
ER  -},
	biburl = {http://www.bibsonomy.org/bibtex/25fbd24f07fe8784b516e69b0eb3192f3/wnpxrz},
	keywords = {recommendersystems social collaborative filtering tagging bookmarking}
}

@inproceedings{breese98empirical,
        title = {Empirical Analysis of Predictive Algorithms for Collaborative Filtering},
        author = {John S. Breese and David Heckerman and Carl Kadie},
        booktitle = {Proceedings of the 14$^{th}$ Conference on Uncertainty in Artificial Intelligence},
        pages = {43-52},
        year = 1998,
        biburl = {http://www.bibsonomy.org/bibtex/282cd7b6c312f4181b1d05adb10c1d56a/wnpxrz},
	keywords = {filtering collaborative evaluation}
}

@misc{si03flexible,
        title = {A Flexible Mixture Model for Collaborative Filtering},
        author = {L. Si and R. Jin},
        year = 2003,
        url = {citeseer.ist.psu.edu/si03flexible.html},
        description = {Flexible Mixture Model for Collaborative Filtering - Si, Jin (ResearchIndex)},
	biburl = {http://www.bibsonomy.org/bibtex/250caac59e67d472076003c36a44b1f15/wnpxrz},
	keywords = {imported filtering mixture model collaborative}
}

@misc{jin-automatic,
        title = {An Automatic Weighting Scheme for Collaborative Filtering},
        author = {Rong Jin and Joyce Y. Chai and Luo Si},
        year = 2004,
        url = {citeseer.ist.psu.edu/jin04automatic.html},
        description = {An Automatic Weighting Scheme for Collaborative Filtering (ResearchIndex)},
	biburl = {http://www.bibsonomy.org/bibtex/2eff7622cbaaf92565e8f1703ba78f67e/wnpxrz},
	keywords = {collaborative imported filtering weighting}
}