| Author | Title | Year | Journal/Proceedings | Reftype | DOI/URL |
|---|---|---|---|---|---|
| Bogers, T. & van den Bosch, A. | Recommending scientific articles using citeulike [BibTeX] |
2008 | RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems, pp. 287-290 | inproceedings | DOI URL |
BibTeX:
@inproceedings{1454053,
author = {Bogers, Toine and van den Bosch, Antal},
title = {Recommending scientific articles using citeulike},
booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems},
publisher = {ACM},
year = {2008},
pages = {287--290},
url = {http://portal.acm.org/citation.cfm?id=1454053},
doi = {http://doi.acm.org/10.1145/1454008.1454053}
}
|
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| Heymann, P., Ramage, D. & Garcia-Molina, H. | Social tag prediction | 2008 | SIGIR '08: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 531-538 | inproceedings | DOI URL |
| Abstract: In this paper, we look at the "social tag prediction" problem. Given a set of objects, and a set of tags applied to those objects by users, can we predict whether a given tag could/should be applied to a particular object? We investigated this question using one of the largest crawls of the social bookmarking system del.icio.us gathered to date. For URLs in del.icio.us, we predicted tags based on page text, anchor text, surrounding hosts, and other tags applied to the URL. We found an entropy-based metric which captures the generality of a particular tag and informs an analysis of how well that tag can be predicted. We also found that tag-based association rules can produce very high-precision predictions as well as giving deeper understanding into the relationships between tags. Our results have implications for both the study of tagging systems as potential information retrieval tools, and for the design of such systems. | |||||
BibTeX:
@inproceedings{heymann2008social,
author = {Heymann, Paul and Ramage, Daniel and Garcia-Molina, Hector},
title = {Social tag prediction},
booktitle = {SIGIR '08: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
publisher = {ACM},
year = {2008},
pages = {531--538},
url = {http://portal.acm.org/citation.cfm?id=1390334.1390425},
doi = {http://doi.acm.org/10.1145/1390334.1390425}
}
|
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| Symeonidis, P., Nanopoulos, A. & Manolopoulos, Y. | Tag recommendations based on tensor dimensionality reduction [BibTeX] |
2008 | RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems, pp. 43-50 | inproceedings | DOI URL |
BibTeX:
@inproceedings{1454017,
author = {Symeonidis, Panagiotis and Nanopoulos, Alexandros and Manolopoulos, Yannis},
title = {Tag recommendations based on tensor dimensionality reduction},
booktitle = {RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems},
publisher = {ACM},
year = {2008},
pages = {43--50},
url = {http://portal.acm.org/citation.cfm?id=1454017},
doi = {http://doi.acm.org/10.1145/1454008.1454017}
}
|
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| Vig, J., Sen, S. & Riedl, J. | Tagsplanations: explaining recommendations using tags | 2008 | IUI '09: Proceedingsc of the 13th international conference on Intelligent user interfaces, pp. 47-56 | inproceedings | DOI URL |
| Abstract: While recommender systems tell users what items they might like, explanations of recommendations reveal why they might like them. Explanations provide many benefits, from improving user satisfaction to helping users make better decisions. This paper introduces tagsplanations, which are explanations based on community tags. Tagsplanations have two key components: tag relevance, the degree to which a tag describes an item, and tag preference, the user's sentiment toward a tag. We develop novel algorithms for estimating tag relevance and tag preference, and we conduct a user study exploring the roles of tag relevance and tag preference in promoting effective tagsplanations. We also examine which types of tags are most useful for tagsplanations. | |||||
BibTeX:
@inproceedings{1502661,
author = {Vig, Jesse and Sen, Shilad and Riedl, John},
title = {Tagsplanations: explaining recommendations using tags},
booktitle = {IUI '09: Proceedingsc of the 13th international conference on Intelligent user interfaces},
publisher = {ACM},
year = {2008},
pages = {47--56},
url = {http://portal.acm.org/citation.cfm?id=1502650.1502661},
doi = {http://doi.acm.org/10.1145/1502650.1502661}
}
|
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| Höhfeld, S. & Kwiatkowski, M. | Empfehlungssysteme aus informationswissenschaftlicher Sicht-State of the Art | 2007 | IWP-Information Wissenschaft & Praxis Vol. 58(5), pp. 265-276 |
article | URL |
| Abstract: Empfehlungssysteme tragen Inhalte individuell
Nutzer im WWW heran, sierend auf deren konkreten Bedürfnissen, rlieben und Interessen. Solche steme können Produkte, Services, tzer (mit analogen Interessen) uvm. rschlagen und stellen damit – gerade Web 2.0-Zeitalter – eine besondere rm der Personalisierung sowie des cial networking dar. Damit bieten pfehlungssysteme Anbietern im ECommerce nen entscheidenden rktvorteil, weshalb die Auswertung r Kundendaten bei großen Firmen e Amazon, Google oder Ebay eine he Priorität besitzt. Aus diesem und wird im vorliegenden Artikel auf e Ansätze von Empfehlungssystemen, lche auf unterschiedliche Weise e Bedürfnisse des Nutzers aufgreifen w. „vorausahnen“ und ihm Vorschläge us verschiedenen Bereichen) terbreiten können, eingegangen. Der tikel liefert eine Definition und Darstellung r Arbeitsweisen von Empfehlungssystemen. bei werden die rschiedenen Methodiken jener enste vergleichend erläutert, um ihre weiligen Vor- und Nachteile deutlich machen. Außerdem wird der Ontologie- d Folksonomy-Einsatz innerhalb n Empfehlungssystemen beleuchtet, Chancen und Risiken der Anwendung n Methoden der Wissensrepräsentation r zukünftige Forschungsarbeiten nschätzen zu können. commender Systems in an Information ience View – The State of the Art commender systems offer content dividually to users in the WWW, sed on their concrete needs, preferences d interests. Those systems n propose products, services, users ith analogous interests), etc.) and present a special form of personalisation well as of social networking exactly in the Web 2.0 age. Recommender stems offer e.g. suppliers in e e-commerce a crucial market advantage. , the evaluation of the customer ta has high priority at big mpanies like Amazon, Google or ay. For this reason we engaged in commender systems, which take up e user’s needs in different ways, to nticipate“ needs and make suggestions rom different areas) to the user. is review article achieves a definition d representation of operations d methods of recommender systems. actly the different methodologies those services should be expounded mparativly on that occasion order to represent advantages d disadvantages. The use of ontologies d folksonomies as implementations recommender systems is portrayed order to be able to take into nsideration chances and risks of the plication of knowledge representation thods for future researches. |
|||||
BibTeX:
@article{ieKey,
author = {Höhfeld, Stefanie and Kwiatkowski, Melanie},
title = {Empfehlungssysteme aus informationswissenschaftlicher Sicht-State of the Art},
journal = {IWP-Information Wissenschaft & Praxis},
year = {2007},
volume = {58},
number = {5},
pages = {265-276},
url = {http://wwwalt.phil-fak.uni-duesseldorf.de/infowiss/admin/public_dateien/files/58/1189509550empfehlung.pdf}
}
|
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| Niwa, S., Doi, T. & Honiden, S. | Web Page Recommender System based on Folksonomy Mining [BibTeX] |
2006 | ITNG '06: Proceedings of the Third International Conference on Information Technology: New Generations (ITNG'06), pp. 388-393 | inproceedings | DOI URL |
BibTeX:
@inproceedings{1128138,
author = {Niwa, Satoshi and Doi, Takuo and Honiden, Shinichi},
title = {Web Page Recommender System based on Folksonomy Mining},
booktitle = {ITNG '06: Proceedings of the Third International Conference on Information Technology: New Generations (ITNG'06)},
publisher = {IEEE Computer Society},
year = {2006},
pages = {388--393},
url = {http://portal.acm.org/citation.cfm?id=1128138},
doi = {http://dx.doi.org/10.1109/ITNG.2006.140}
}
|
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| Xu, Y., Zhang, L. & Liu, W. | Cubic Analysis of Social Bookmarking for Personalized Recommendation | 2006 | Frontiers of WWW Research and Development - APWeb 2006, pp. 733-738 | article | URL |
| 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 usersâ various interests, and make better personalized recommendation based on them. Experiment reveals the superiority of our method over traditional CF methods. ER - | |||||
BibTeX:
@article{keyhere,
author = {Xu, Yanfei and Zhang, Liang and Liu, Wei},
title = {Cubic Analysis of Social Bookmarking for Personalized Recommendation},
journal = {Frontiers of WWW Research and Development - APWeb 2006},
year = {2006},
pages = {733--738},
url = {http://dx.doi.org/10.1007/11610113_66}
}
|
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