| Author | Title | Year | Journal/Proceedings | Reftype | DOI/URL |
|---|---|---|---|---|---|
| Adrian, B., Sauermann, L. & Roth-Berghofer, T. | ConTag: A semantic tag recommendation system | 2007 | Proceedings of I-Semantics' 07, pp. pp. 297-304 | inproceedings | DOI URL |
| Abstract: ConTag is an approach to generate semantic tag recommendations for documents based on Semantic Web ontologies and Web 2.0 services. We designed and implemented a process to normalize documents to RDF format, extract document topics using Web 2.0 services and finally match extracted topics to a Semantic Web ontology. Due to ConTag we are able to show that the information provided by Web 2.0 services in combination with a Semantic Web ontology enables the generation of relevant semantic tag recommendations for documents. The main contribution of this work is a semantic tag recommendation process based on a choreography of Web 2.0 services. |
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BibTeX:
@inproceedings{adrian+2007a,
author = {Benjamin Adrian and Leo Sauermann and Thomas Roth-Berghofer},
title = {ConTag: A semantic tag recommendation system},
booktitle = {Proceedings of I-Semantics' 07},
publisher = {JUCS},
year = {2007},
pages = {pp. 297-304},
url = {http://www.dfki.uni-kl.de/~sauermann/papers/horak+2007a.pdf},
doi = {ISSN 0948-6968}
}
|
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| Basu, C., Hirsh, H. & Cohen, W. | Recommendation as classification: using social and content-based information in recommendation [BibTeX] |
1998 | AAAI '98/IAAI '98: Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence, pp. 714-720 | inproceedings | URL |
BibTeX:
@inproceedings{295795,
author = {Chumki Basu and Haym Hirsh and William Cohen},
title = {Recommendation as classification: using social and content-based information in recommendation},
booktitle = {AAAI '98/IAAI '98: Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence},
publisher = {American Association for Artificial Intelligence},
year = {1998},
pages = {714--720},
url = {ftp://ftp.cs.rutgers.edu/pub/hirsh/papers/1998/aaai1.ps}
}
|
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| Herlocker, J.L., Konstan, J.A., Terveen, L.G. & Riedl, J.T. | Evaluating collaborative filtering recommender systems | 2004 | ACM Trans. Inf. Syst. Vol. 22(1), pp. 5-53 |
article | DOI URL |
| 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. | |||||
BibTeX:
@article{herlocker2004evaluating,
author = {Jonathan L. Herlocker and Joseph A. Konstan and Loren G. Terveen and John T. Riedl},
title = {Evaluating collaborative filtering recommender systems},
journal = {ACM Trans. Inf. Syst.},
publisher = {ACM Press},
year = {2004},
volume = {22},
number = {1},
pages = {5--53},
url = {http://portal.acm.org/citation.cfm?id=963770.963772},
doi = {http://doi.acm.org/10.1145/963770.963772}
}
|
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| Lin, W., Alvarez, S. & Ruiz, C. | Efficient adaptive-support association rule mining for recommender systems [BibTeX] |
2002 | Data Mining and Knowledge Discovery Vol. 6, pp. 83-105 |
article | |
BibTeX:
@article{Linetal02,
author = {W. Lin and S.A. Alvarez and C. Ruiz},
title = {Efficient adaptive-support association rule mining for recommender systems},
journal = {Data Mining and Knowledge Discovery},
year = {2002},
volume = {6},
pages = {83--105}
}
|
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| Middleton, S.E., Alani, H., Shadbolt, N. & Roure, D.D. | Exploiting Synergy Between Ontologies and Recommender Systems. [BibTeX] |
2002 | Vol. 55Semantic Web Workshop |
inproceedings | URL |
BibTeX:
@inproceedings{conf/sww/MiddletonASR02,
author = {Stuart E. Middleton and Harith Alani and Nigel Shadbolt and David De Roure},
title = {Exploiting Synergy Between Ontologies and Recommender Systems.},
booktitle = {Semantic Web Workshop},
publisher = {CEUR-WS.org},
year = {2002},
volume = {55},
url = {http://dblp.uni-trier.de/db/conf/sww/sww2002.html#MiddletonASR02}
}
|
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| Niwa, S., Doi, T. & Honiden, S. | Web Page Recommender System based on Folksonomy Mining for ITNG 06 Submissions | 2006 | Information Technology: New Generations, 2006. ITNG 2006. Third International Conference on, pp. 388- 393 | inproceedings | DOI URL |
| 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. | |||||
BibTeX:
@inproceedings{Niwa:2006,
author = {S. Niwa and Takuo Doi and S. Honiden},
title = {Web Page Recommender System based on Folksonomy Mining for ITNG 06 Submissions},
booktitle = {Information Technology: New Generations, 2006. ITNG 2006. Third International Conference on},
year = {2006},
pages = {388- 393},
url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1611624},
doi = {http://dx.doi.org/10.1109/ITNG.2006.140}
}
|
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| Rashid, A.M., Karypis, G. & Riedl, J. | Influence in ratings-based recommender systems: An algorithm-independent approach [BibTeX] |
2005 | Proceedings of the SIAM International Conference on Data Mining | article | URL |
BibTeX:
@article{rashid2005irb,
author = {A. M. Rashid and G. Karypis and J. Riedl},
title = {Influence in ratings-based recommender systems: An algorithm-independent approach},
journal = {Proceedings of the SIAM International Conference on Data Mining},
year = {2005},
url = {http://www.grouplens.org/papers/pdf/RashidAl_siam05.pdf}
}
|
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| Terveen, L. & Hill, W. | Beyond Recommender Systems: Helping People Help Each Other [BibTeX] |
2001 | misc | URL | |
BibTeX:
@misc{terveen01beyond,
author = {L. Terveen and W. Hill},
title = {Beyond Recommender Systems: Helping People Help Each Other},
year = {2001},
url = {citeseer.ist.psu.edu/terveen01beyond.html}
}
|
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| Wang, Y., Wang, H., Zhu, H. & Yu, Y. | Exploit Semantic Information for Category Annotation Recommendation in Wikipedia | 2007 | Natural Language Processing and Information Systems, pp. 48-60 | article | URL |
| Abstract: Compared with plain-text resources, the ones in âsemi-semanticâ web sites, such as Wikipedia, contain high-level semantic information which will benefit various automatically annotating tasks on themself. In this paper, we propose a âcollaborativeannotatingâ approach to automatically recommend categories for a Wikipedia article by reusing category annotations from itsmost similar articles and ranking these annotations by their confidence. In this approach, four typical semantic featuresin Wikipedia, namely incoming link, outgoing link, section heading and template item, are investigated and exploited as therepresentation of articles to feed the similarity calculation. The experiment results have not only proven that these semanticfeatures improve the performance of category annotating, with comparison to the plain text feature; but also demonstratedthe strength of our approach in discovering missing annotations and proper level ones for Wikipedia articles. | |||||
BibTeX:
@article{keyhere,
author = {Yang Wang and Haofen Wang and Haiping Zhu and Yong Yu},
title = {Exploit Semantic Information for Category Annotation Recommendation in Wikipedia},
journal = {Natural Language Processing and Information Systems},
year = {2007},
pages = {48--60},
url = {http://dx.doi.org/10.1007/978-3-540-73351-5_5}
}
|
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| Zhang, Z. & Nasraoui, O. | Mining search engine query logs for query recommendation [BibTeX] |
2006 | Proceedings of the 15th international conference on World Wide Web, pp. 1039-1040 | article | |
BibTeX:
@article{zhang2006,
author = {Z. Zhang and O. Nasraoui},
title = {Mining search engine query logs for query recommendation},
journal = {Proceedings of the 15th international conference on World Wide Web},
publisher = {ACM Press New York, NY, USA},
year = {2006},
pages = {1039--1040}
}
|
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Created by JabRef on 30/08/2008.