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		<title>Collaborative Preference Learning - Versionsgeschichte</title>
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		<id>http://de.evo-art.org/index.php?title=Collaborative_Preference_Learning&amp;diff=32426&amp;oldid=prev</id>
		<title>Gubachelier am 30. November 2015 um 11:09 Uhr</title>
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				<updated>2015-11-30T11:09:19Z</updated>
		
		<summary type="html">&lt;p&gt;&lt;/p&gt;
&lt;table class=&quot;diff diff-contentalign-left&quot; data-mw=&quot;interface&quot;&gt;
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				&lt;td colspan=&#039;2&#039; style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;← Nächstältere Version&lt;/td&gt;
				&lt;td colspan=&#039;2&#039; style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;Version vom 30. November 2015, 11:09 Uhr&lt;/td&gt;
				&lt;/tr&gt;&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot; id=&quot;mw-diff-left-l2&quot; &gt;Zeile 2:&lt;/td&gt;
&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot;&gt;Zeile 2:&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class=&#039;diff-marker&#039;&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;/td&gt;&lt;td class=&#039;diff-marker&#039;&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class=&#039;diff-marker&#039;&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;== Reference ==&lt;/div&gt;&lt;/td&gt;&lt;td class=&#039;diff-marker&#039;&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;== Reference ==&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class=&#039;diff-marker&#039;&gt;−&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;Alexandros Karatzoglou, Markus Weimer: [[Collaborative Preference Learning]]. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 409-427. &amp;#160;&lt;/div&gt;&lt;/td&gt;&lt;td class=&#039;diff-marker&#039;&gt;+&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;Alexandros Karatzoglou, Markus Weimer: [[Collaborative Preference Learning]]. In: Fürnkranz, J. and Hüllermeier, E.: &lt;ins class=&quot;diffchange diffchange-inline&quot;&gt;[[&lt;/ins&gt;Preference Learning&lt;ins class=&quot;diffchange diffchange-inline&quot;&gt;]]&lt;/ins&gt;, 2011, 409-427. &amp;#160;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class=&#039;diff-marker&#039;&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;/td&gt;&lt;td class=&#039;diff-marker&#039;&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class=&#039;diff-marker&#039;&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;== DOI ==&lt;/div&gt;&lt;/td&gt;&lt;td class=&#039;diff-marker&#039;&gt;&amp;#160;&lt;/td&gt;&lt;td style=&quot;background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;== DOI ==&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;/table&gt;</summary>
		<author><name>Gubachelier</name></author>	</entry>

	<entry>
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		<title>Gubachelier: Die Seite wurde neu angelegt: „  == Reference == Alexandros Karatzoglou, Markus Weimer: Collaborative Preference Learning. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2…“</title>
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				<updated>2015-11-30T10:48:08Z</updated>
		
		<summary type="html">&lt;p&gt;Die Seite wurde neu angelegt: „  == Reference == Alexandros Karatzoglou, Markus Weimer: &lt;a href=&quot;/index.php?title=Collaborative_Preference_Learning&quot; title=&quot;Collaborative Preference Learning&quot;&gt;Collaborative Preference Learning&lt;/a&gt;. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2…“&lt;/p&gt;
&lt;p&gt;&lt;b&gt;Neue Seite&lt;/b&gt;&lt;/p&gt;&lt;div&gt;&lt;br /&gt;
&lt;br /&gt;
== Reference ==&lt;br /&gt;
Alexandros Karatzoglou, Markus Weimer: [[Collaborative Preference Learning]]. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 409-427. &lt;br /&gt;
&lt;br /&gt;
== DOI ==&lt;br /&gt;
http://dx.doi.org/10.1007/978-3-642-14125-6_19 &lt;br /&gt;
&lt;br /&gt;
== Abstract ==&lt;br /&gt;
Every recommender system needs the notion of preferences of a user to suggest one item and not another. However, current recommender algorithms deduct these preferences by first predicting an actual rating of the items and then sorting those. Departing from this, we present an algorithm that is capable of directly learning the preference function from given ratings. The presented approach combines recent results on preference learning, state-of-the-art optimization algorithms, and the large margin approach to capacity control. The algorithm follows the matrix factorization paradigm to collaborative filtering. Maximum Margin Matrix Factorization (MMMF) has been introduced to control the capacity of the prediction to avoid overfitting. We present an extension to this approach that is capable of using the methodology developed by the Learning to Rank community to learn a ranking of unrated items for each user. In addition, we integrate several recently proposed extensions to MMMF into one coherent framework where they can be combined in a mix-and-match fashion.&lt;br /&gt;
&lt;br /&gt;
== Extended Abstract ==&lt;br /&gt;
&lt;br /&gt;
== Bibtex == &lt;br /&gt;
 @incollection{&lt;br /&gt;
 year={2011},&lt;br /&gt;
 isbn={978-3-642-14124-9},&lt;br /&gt;
 booktitle={Preference Learning},&lt;br /&gt;
 editor={Fürnkranz, Johannes and Hüllermeier, Eyke},&lt;br /&gt;
 doi={10.1007/978-3-642-14125-6_19},&lt;br /&gt;
 title={Collaborative Preference Learning},&lt;br /&gt;
 url={http://dx.doi.org/10.1007/978-3-642-14125-6_19, http://de.evo-art.org/index.php?title=Collaborative_Preference_Learning },&lt;br /&gt;
 publisher={Springer Berlin Heidelberg},&lt;br /&gt;
 author={Karatzoglou, Alexandros and Weimer, Markus},&lt;br /&gt;
 pages={409-427},&lt;br /&gt;
 language={English}&lt;br /&gt;
 }&lt;br /&gt;
&lt;br /&gt;
== Used References ==&lt;br /&gt;
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3. C.J. Burges, Q.V. Le, R. Ragno, Learning to rank with nonsmooth cost functions, in Advances in Neural Information Processing Systems (NIPS), vol. 19, ed. by B. Schölkopf, J. Platt, T. Hofmann (2007), pp. 193–200&lt;br /&gt;
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4. O. Chapelle, Q.V. Le, A. Smola, Large margin optimization of ranking measures, in NIPS Workshop: Machine Learning for Web Search (2007)&lt;br /&gt;
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6. R. Herbrich, T. Graepel, K. Obermayer, Large margin rank boundaries for ordinal regression, in Advances in Large Margin Classifiers, ed. by A.J. Smola, P.L. Bartlett, B. Schölkopf, D. Schuurmans (MIT, Cambridge, MA, 2000), pp. 115–132&lt;br /&gt;
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7. T. Hofmann, Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. (TOIS) 22(1), 89–115 (2004)&lt;br /&gt;
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8. T. Joachims, Training linear SVMs in linear time, in Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD) (ACM, 2006), pp. 217–226&lt;br /&gt;
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12. R. Salakhutdinov, A. Mnih, Probabilistic matrix factorization, in Advances in Neural Information Processing Systems (NIPS), vol. 20 (MIT, Cambridge, MA, 2008)&lt;br /&gt;
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13. A. Smola, S.V.N. Vishwanathan, Q. Le, Bundle methods for machine learning, in Advances in Neural Information Processing Systems (NIPS), vol. 20 (MIT, Cambridge, MA, 2008)&lt;br /&gt;
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14. A.J. Smola, I.R. Kondor, Kernels and regularization on graphs, in Proceedings of the Annual Conference on Computational Learning Theory (COLT), Lecture Notes in Computer Science, ed. by B. Schölkopf, M.K. Warmuth (Springer, Heidelberg, Germany, 2003), pp. 144–158&lt;br /&gt;
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17. N. Srebro, A. Shraibman, Rank, trace-norm and max-norm, in Proceedings of the Annual Conference on Computational Learning Theory (COLT), vol. 3559, Lecture Notes in Artificial Intelligence, ed. by P. Auer, R. Meir (Springer, 2005), pp. 545–560&lt;br /&gt;
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    &lt;br /&gt;
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23. M. Weimer, A. Karatzoglou, A. Smola, Improving maximum margin matrix factorization. Mach. Learn. 72(3), 263–276 (2008) http://dx.doi.org/10.1007/s10994-008-5073-7&lt;br /&gt;
    &lt;br /&gt;
24. M. Weimer, A. Karatzoglou, A. Smola, Improving maximum margin matrix factorization, in Machine Learning and Knowledge Discovery in Databases, vol. 5211, LNAI, ed. by W. Daelemans, B. Goethals, K. Morik (Springer, 2008), pp. 14–14&lt;br /&gt;
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25. J. Yu, S.V.N. Vishwanathan, S. Günter, N.N. Schraudolph, A quasi-Newton approach to nonsmooth convex optimization, In Proceedings of the 25th International Conference on Machine Learning (ICML), ed. by A. McCallum, S. Roweis (Omnipress, 2008.), pp. 1216–1223&lt;br /&gt;
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26. S. Yu, K. Yu, V. Tresp, H.P. Kriegel, Collaborative ordinal regression, in Proceedings of the 23rd International Conference on Machine Learning (ICML), ed. by W.W. Cohen, A. Moore (ACM, 2006), pp. 1089–1096&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
== Links ==&lt;br /&gt;
=== Full Text === &lt;br /&gt;
http://www.researchgate.net/publication/241276611&lt;br /&gt;
&lt;br /&gt;
[[intern file]]&lt;br /&gt;
&lt;br /&gt;
=== Sonstige Links ===&lt;/div&gt;</summary>
		<author><name>Gubachelier</name></author>	</entry>

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