@article{jaeschke2008tag, abstract = {Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender. }, address = {Amsterdam}, author = {Jäschke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd}, doi = {10.3233/AIC-2008-0438}, editor = {Giunchiglia, Enrico}, interhash = {b2f1aba6829affc85d852ea93a8e39f7}, intrahash = {955bcf14f3272ba6eaf3dadbef6c0b10}, issn = {0921-7126}, journal = {AI Communications}, month = dec, number = 4, pages = {231--247}, publisher = {IOS Press}, title = {Tag Recommendations in Social Bookmarking Systems}, url = {http://www.kde.cs.uni-kassel.de/pub/pdf/jaeschke2008tag.pdf}, vgwort = {63}, volume = 21, year = 2008 } @inproceedings{cattuto2008semantic, abstract = {Social bookmarking systems allow users to organise collections of resources on the Web in a collaborative fashion. The increasing popularity of these systems as well as first insights into their emergent semantics have made them relevant to disciplines like knowledge extraction and ontology learning. The problem of devising methods to measure the semantic relatedness between tags and characterizing it semantically is still largely open. Here we analyze three measures of tag relatedness: tag co-occurrence, cosine similarity of co-occurrence distributions, and FolkRank, an adaptation of the PageRank algorithm to folksonomies. Each measure is computed on tags from a large-scale dataset crawled from the social bookmarking system del.icio.us. To provide a semantic grounding of our findings, a connection to WordNet (a semantic lexicon for the English language) is established by mapping tags into synonym sets of WordNet, and applying there well-known metrics of semantic similarity. Our results clearly expose different characteristics of the selected measures of relatedness, making them applicable to different subtasks of knowledge extraction such as synonym detection or discovery of concept hierarchies.}, address = {Patras, Greece}, author = {Cattuto, Ciro and Benz, Dominik and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proceedings of the 3rd Workshop on Ontology Learning and Population (OLP3)}, interhash = {cc62b733f6e0402db966d6dbf1b7711f}, intrahash = {3b0aca61b24e4343bd80390614e3066e}, isbn = {978-960-89282-6-8}, month = jul, pages = {39--43}, title = {Semantic Analysis of Tag Similarity Measures in Collaborative Tagging Systems}, url = {http://olp.dfki.de/olp3/}, year = 2008 } @inproceedings{benz07ontology, abstract = {The emergence of collaborative tagging systems with their underlying flat and uncontrolled resource organization paradigm has led to a large number of research activities focussing on a formal description and analysis of the resulting "folksonomies". An interesting outcome is that the characteristic qualities of these systems seem to be inverse to more traditional knowledge structuring approaches like taxonomies or ontologies: The latter provide rich and precise semantics, but suffer - amongst others - from a knowledge acquisition bottleneck. An important step towards exploiting the possible synergies by bridging the gap between both paradigms is the automatic extraction of relations between tags in a folksonomy. This position paper presents preliminary results of ongoing work to induce hierarchical relationships among tags by analyzing the aggregated data of collaborative tagging systems as a basis for an ontology learning procedure. }, address = {Halle/Saale}, author = {Benz, Dominik and Hotho, Andreas}, booktitle = {Workshop Proceedings of Lernen - Wissensentdeckung - Adaptivität (LWA 2007)}, editor = {Hinneburg, Alexander}, interhash = {ff7de5717f771dabd764675279ff3adf}, intrahash = {72bff5ebe5dfb5023f62ba9b94e6ed01}, isbn = {978-3-86010-907-6}, month = sep, pages = {109--112}, publisher = {Martin-Luther-Universität Halle-Wittenberg}, title = {Position Paper: Ontology Learning from Folksonomies}, url = {http://lwa07.informatik.uni-halle.de/kdml07/kdml07.htm}, year = 2007 } @article{voelker2008aeon, abstract = {OntoClean is an approach towards the formal evaluation of taxonomic relations in ontologies. The application of OntoClean consists of two main steps. First, concepts are tagged according to meta-properties known as rigidity, unity, dependency and identity. Second, the tagged concepts are checked according to predefined constraints to discover taxonomic errors. Although OntoClean is well documented in numerous publications, it is still used rather infrequently due to the high costs of application. Especially, the manual tagging of concepts with the correct meta-properties requires substantial efforts of highly experienced ontology engineers. In order to facilitate the use of OntoClean and to enable the evaluation of real-world ontologies, we provide AEON, a tool which automatically tags concepts with appropriate OntoClean meta-properties and performs the constraint checking. We use the Web as an embodiment of world knowledge, where we search for patterns that indicate how to properly tag concepts. We thoroughly evaluated our approach against a manually created gold standard. The evaluation shows the competitiveness of our approach while at the same time significantly lowering the costs. All of our results, i.e. the tool AEON as well as the experiment data, are publicly available.}, address = {Amsterdam, The Netherlands, The Netherlands}, author = {Völker, Johanna and Vrandečić, Denny and Sure, York and Hotho, Andreas}, interhash = {f14794f4961d0127dc50c1938eaef7ea}, intrahash = {f8f0bb3e3495e7627770b470d1a5f1a3}, issn = {1570-5838}, journal = {Applied Ontology}, number = {1-2}, pages = {41--62}, publisher = {IOS Press}, title = {AEON - An approach to the automatic evaluation of ontologies}, url = {http://portal.acm.org/citation.cfm?id=1412422}, volume = 3, year = 2008 } @inproceedings{middleton02, abstract = {Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations. Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured.}, author = {Middleton, Stuart E. and Alani, Harith and Roure, David C. De}, booktitle = {Proceedings of the WWW2002 International Workshop on the Semantic Web}, interhash = {a098783b2b8f386218c3312ebcfa6286}, intrahash = {401e667028f6a4674bb5403ec680d7f3}, note = {cite arxiv:cs.LG/0204012 Comment: Semantic web conference, WWW2002, 10 pages}, title = {Exploiting Synergy Between Ontologies and Recommender Systems}, url = {http://arxiv.org/abs/cs/0204012}, year = 2002 } @article{romero07, abstract = {Currently there is an increasing interest in data mining and educational systems, making educational data mining as a new growing research community. This paper surveys the application of data mining to traditional educational systems, particular web-based courses, well-known learning content management systems, and adaptive and intelligent web-based educational systems. Each of these systems has different data source and objectives for knowledge discovering. After preprocessing the available data in each case, data mining techniques can be applied: statistics and visualization; clustering, classification and outlier detection; association rule mining and pattern mining; and text mining. The success of the plentiful work needs much more specialized work in order for educational data mining to become a mature area.}, address = {Tarrytown, NY, USA}, author = {Romero, C. and Ventura, S.}, doi = {http://dx.doi.org/10.1016/j.eswa.2006.04.005}, interhash = {89d843f1a3b181f2a628e881d9210b22}, intrahash = {746d12e92e58587461ffcb8dc381e283}, issn = {0957-4174}, journal = {Expert Syst. Appl.}, number = 1, pages = {135--146}, publisher = {Pergamon Press, Inc.}, title = {Educational data mining: A survey from 1995 to 2005}, url = {http://portal.acm.org/citation.cfm?id=1223659}, volume = 33, year = 2007 } @inproceedings{tang03, author = {Tang, Tiffany Ya and Mccalla, Gordon}, booktitle = {Workshop on Technologies for Electronic Documents for Supporting Learning, International Conference on Artificial Intelligence in Education (AIED}, interhash = {bcf301fcfe413916c8b313de663bc866}, intrahash = {129771e6d0d33513e1729549f98247a5}, title = {Smart Recommendation for an Evolving E-Learning System}, url = {http://www.cs.usyd.edu.au/~aied/vol10/vol10_TangMcCalla.pdf}, year = 2003 } @inproceedings{zaiane2002building, abstract = {A recommender system in an e-learning context is a software agent that tries to "intelligently" recommend actions to a learner based on the actions of previous learners. This recommendation could be an on-line activity such as doing an exercise, reading posted messages on a conferencing system, or running an on-line simulation, or could be simply a web resource. These recommendation systems have been tried in e-commerce to entice purchasing of goods, but haven't been tried in e-learning. This paper suggests the use of web mining techniques to build such an agent that could recommend on-line learning activities or shortcuts in a course web site based on learners' access history to improve course material navigation as well as assist the online learning process. These techniques are considered integrated web mining as opposed to off-line web mining used by expert users to discover on-line access patterns.}, address = {Washington, DC, USA }, author = {Zaiane, O.R.}, booktitle = {Proceedings of the International Conference on Computers in Education}, doi = {10.1109/CIE.2002.1185862}, interhash = {fce17e1e38b714031e92357f6f5877f5}, intrahash = {df8fdf1b3452eabdecd64d0f3f4472dc}, isbn = {0-7695-1509-6}, month = dec, organization = {IEEE Computer Society}, pages = {55--59}, title = {Building a recommender agent for e-learning systems}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1185862}, volume = 1, year = 2002 } @article{rafaeli05, abstract = {Recommendation systems can play an extensive role in online learning. In such systems, learners can receive guidance in locating and ranking references, knowledge bits, test items, and so forth. In recommender systems, users’ ratings can be applied toward items, users, other users’ ratings, and, if allowed, raters of raters of items recursively. In this chapter, we describe an online learning system — QSIA — an active recommender system for Questions Sharing and Interactive Assignments, designed to enhance knowledge sharing among learners. First, we lay out some of the theoretical background for social, open-rating mechanisms in online learning systems. We discuss concepts such as social versus black-box recommendations and the advice of neighbors as opposed to that of friends. We argue that enabling subjective views and ratings of other users is an inevitable phase of social collaboration systems. We also argue that social recommendations are critical for the exploitation of the value associated with recommendation.}, author = {Rafaeli, Sheizaf and Dan-Gur, Yuval and Barak, Miri}, editor = {Jin, Qun}, interhash = {313fc441724733ba4bbeb928c721a481}, intrahash = {450dea33236d00ed7f6f645b6f91ff3e}, issn = {1539-3100}, journal = {International Journal of Distance Education Technologies}, number = 2, pages = {30-47}, publisher = {Idea Group Publishing}, title = {Social Recommender Systems: Recommendations in Support of E-Learning}, url = {http://www.igi-global.com/articles/details.asp?id=4784}, volume = 3, year = 2005 } @article{chen05, abstract = {Personalized service is important on the Internet, especially in Web-based learning. Generally, most personalized systems consider learner preferences, interests, and browsing behaviors in providing personalized services. However, learner ability usually is neglected as an important factor in implementing personalization mechanisms. Besides, too many hyperlink structures in Web-based learning systems place a large information burden on learners. Consequently, in Web-based learning, disorientation (losing in hyperspace), cognitive overload, lack of an adaptive mechanism, and information overload are the main research issues. This study proposes a personalized e-learning system based on Item Response Theory (PEL-IRT) which considers both course material difficulty and learner ability to provide individual learning paths for learners. The item characteristic function proposed by Rasch with a single difficulty parameter is used to model the course materials. To obtain more precise estimation of learner ability, the maximum likelihood estimation (MLE) is applied to estimate learner ability based on explicit learner feedback. Moreover, to determine an appropriate level of difficulty parameter for the course materials, this study also proposes a collaborative voting approach for adjusting course material difficulty. Experiment results show that applying Item Response Theory (IRT) to Web-based learning can achieve personalized learning and help learners to learn more effectively and efficiently.}, address = {Oxford, UK, UK}, author = {Chen, Chih-Ming and Lee, Hahn-Ming and Chen, Ya-Hui}, doi = {http://dx.doi.org/10.1016/j.compedu.2004.01.006}, interhash = {c8b0ea1e16a98efd85df7f09594a6247}, intrahash = {6bf0a9a55628bacc1c8a817d4d838687}, issn = {0360-1315}, journal = {Comput. Educ.}, number = 3, pages = {237--255}, publisher = {Elsevier Science Ltd.}, title = {Personalized e-learning system using Item Response Theory}, url = {http://portal.acm.org/citation.cfm?id=1066365}, volume = 44, year = 2005 } @inproceedings{basu98, address = {Menlo Park, CA, USA}, author = {Basu, Chumki and Hirsh, Haym and Cohen, William}, booktitle = {AAAI '98/IAAI '98: Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence}, interhash = {8baccca5d4a5001d79070bbdd5439a84}, intrahash = {90f4b7eab8a7a308c6e077a993cd19d8}, isbn = {0-262-51098-7}, location = {Madison, Wisconsin, United States}, pages = {714--720}, publisher = {American Association for Artificial Intelligence}, title = {Recommendation as classification: using social and content-based information in recommendation}, url = {http://portal.acm.org/citation.cfm?id=295240.295795}, year = 1998 } @inproceedings{fleischman03, abstract = {We examine the problems with automated recommendation systems when information about user preferences is limited. We equate the problem to one of content similarity measurement and apply techniques from Natural Language Processing to the domain of movie recommendation. We describe two algorithms, a naïve word-space approach and a more sophisticated approach using topic signatures, and evaluate their performance compared to baseline, gold standard, and commercial systems.}, address = {New York, NY, USA}, author = {Fleischman, Michael and Hovy, Eduard}, booktitle = {IUI '03: Proceedings of the 8th international conference on Intelligent user interfaces}, doi = {http://doi.acm.org/10.1145/604045.604087}, interhash = {e03b7aa4d0ff1bc505e59e0baf87074d}, intrahash = {2435153caffd1b0e9c75a78dbeafd62b}, isbn = {1-58113-586-6}, location = {Miami, Florida, USA}, pages = {242--244}, publisher = {ACM}, title = {Recommendations without user preferences: a natural language processing approach}, url = {http://portal.acm.org/citation.cfm?id=604087}, year = 2003 } @inproceedings{zimdars01, abstract = {We treat collaborative filtering as a univariate time series problem: given a user’s previous votes, predict the next vote. We describe two families of methods for transforming data to encode time order in ways amenable to of-the-shelf classication and density estimation tools. Using a decision-tree learning tool and two real-world data sets, we compare the results of these approaches to the results of collaborative filtering without ordering information. The improvements in both predictive accuracy and in recommendation quality that we realize advocate the use of predictive algorithms exploiting the temporal order of data.}, address = {San Francisco, CA, USA}, author = {Zimdars, Andrew and Chickering, David Maxwell and Meek, Christopher}, booktitle = {UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence}, interhash = {3dcd7aa85ac877a20275de6f4b5a14f7}, intrahash = {46fe7be68f5b3435411beb632094a60b}, isbn = {1-55860-800-1}, pages = {580--588}, publisher = {Morgan Kaufmann Publishers Inc.}, title = {Using Temporal Data for Making Recommendations}, url = {http://portal.acm.org/citation.cfm?id=720264}, year = 2001 } @inproceedings{middleton01, abstract = {Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a dynamic environment. We explore the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontological representation to extract user preferences. A multi-class approach to paper classification is used, allowing the paper topic taxonomy to be utilised during profile construction. The Quickstep recommender system is presented and two empirical studies evaluate it in a real work setting, measuring the effectiveness of using a hierarchical topic ontology compared with an extendable flat list.}, address = {New York, NY, USA}, author = {Middleton, Stuart E. and Roure, David C. De and Shadbolt, Nigel R.}, booktitle = {K-CAP '01: Proceedings of the 1st international conference on Knowledge capture}, doi = {http://doi.acm.org/10.1145/500737.500755}, interhash = {332dfc15a8f0fc442b47a9a4b740b1bf}, intrahash = {6d0a7792db2c0f96bd0a495a56e57464}, isbn = {1-58113-380-4}, location = {Victoria, British Columbia, Canada}, pages = {100--107}, publisher = {ACM}, title = {Capturing knowledge of user preferences: ontologies in recommender systems}, url = {http://portal.acm.org/citation.cfm?id=500737.500755}, year = 2001 } @inproceedings{felfernig05, address = {Washington, DC, USA}, author = {Felfernig, Alexander}, booktitle = {CEC '05: Proceedings of the Seventh IEEE International Conference on E-Commerce Technology}, doi = {http://dx.doi.org/10.1109/ICECT.2005.57}, interhash = {017b5a7a234a9d2cbd5d2c6b459edd63}, intrahash = {68c17752ba4ef2cec9bd515304fc4a95}, isbn = {0-7695-2277-7}, pages = {92--100}, publisher = {IEEE Computer Society}, title = {Koba4MS: Selling Complex Products and Services Using Knowledge-Based Recommender Technologies}, url = {http://portal.acm.org/citation.cfm?id=1097216}, year = 2005 } @incollection{schafer07, abstract = {One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings. We also discuss how to evaluate CF systems, and the evolution of rich interaction interfaces. We close the chapter with discussions of the challenges of privacy particular to a CF recommendation service and important open research questions in the field.}, address = {Berlin, Heidelberg}, author = {Schafer, J. Ben and Frankowski, Dan and Herlocker, Jon and Sen, Shilad}, booktitle = {The Adaptive Web: Methods and Strategies of Web Personalization}, chapter = 9, editor = {Brusilovsky, Peter and Kobsa, Alfred and Nejdl, Wolfgang}, file = {SpringerLink:2007/SchaferFrankowskiEtAl07p291.pdf:PDF}, interhash = {bccec4b3f6845eff4966c5cab3315509}, intrahash = {1c611c2e32fb3b735c3adcd413e95201}, isbn = {978-3-540-72078-2}, owner = {flint}, pages = {291-324}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2008.02.10}, title = {Collaborative Filtering Recommender Systems}, url = {http://dx.doi.org/10.1007/978-3-540-72079-9_9}, volume = 4321, year = 2007 } @inproceedings{hotho2006information, abstract = {Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment, however, 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.}, address = {Heidelberg}, author = {Hotho, Andreas and Jäschke, Robert and Schmitz, Christoph and Stumme, Gerd}, booktitle = {The Semantic Web: Research and Applications}, editor = {Sure, York and Domingue, John}, interhash = {10ec64d80b0ac085328a953bb494fb89}, intrahash = {3c301945817681d637ee43901c016939}, month = {June}, pages = {411-426}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Information Retrieval in Folksonomies: Search and Ranking}, volume = 4011, year = 2006 }