Feature-based matrix factorization - Revision history

Tqchen: /* Model Formalization */

2011-10-01T02:44:04Z

Model Formalization

2011-10-01T02:39:02Z

Example Information that can be Incorporated into the Model

2011-10-01T01:41:55Z

2011-10-01T01:13:41Z

Related Models

2011-10-01T01:10:30Z

Related Models

2011-10-01T01:08:48Z

Related Models

2011-10-01T01:08:21Z

Related Models

2011-09-24T09:57:13Z

2011-09-24T01:12:00Z

2011-09-24T01:11:36Z

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 <math>y = \mu+\left(\sum_j \gamma_j b^g_j +\sum_j \alpha_j b^u_j + \sum_j \beta_j b^i_j\right) +\left(\sum_j \alpha_j p_j\right)^T\left( \sum_j \beta_j q_j\right)</math>
-We call <math>\gamma </math> global feature, <math>\alpha </math> user feature and <math>\beta </math> item feature. Defining different kinds of features will result in different variants of models.
+We call <math>\gamma </math> global feature, <math>\alpha </math> user feature and <math>\beta </math> item feature. Defining different kinds of features will result in different variants of models. We can add global bias effects to global feature, use user feature to describe user preference and use item feature to describe aspects about the item
 == Example Information that can be Incorporated into the Model ==

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 * User temporal factor( user feature )
 * User implicit/explicit feedback( user feature )
 == Related Models ==

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 If we have a solver for feature-based matrix factorization, we only need to design context-aware or informative collaborative filtering(or ranking) models by feature-defining,
 without engineering efforts for writing codes for each new model.
 == Related Models ==

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 == Related Models ==
-* [[Factorization Machine]]: feature-based matrix factorization can be viewed as a restricted case of factorization machine to distinguish different types of features, allowing us to overcome some shortcomings of FM(e.g: including neighborhood information, exact implementation of rank,svd++ ). We call the model feature-based matrix factorization instead of restricted FM because the idea descends more naturally from matrix factorization.
+* [[Factorization Machine]]: feature-based matrix factorization can be viewed as a restricted case of factorization machine to distinguish different types of features, allowing us to overcome some shortcomings of FM(e.g: including neighborhood information, exact implementation of rank,svd++ ). We call the model feature-based matrix factorization instead of restricted FM because the idea descends more naturally from matrix factorization using features.
 == Implementation ==

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 == Related Models ==
-* [[Factorization Machine]]: feature-based matrix factorization can be viewed as a restricted case of factorization machine to distinguish different types of features, allowing us to overcome some shortcomings of FM. We call the model feature-based matrix factorization instead of restricted FM because the idea descends more naturally from matrix factorization.
+* [[Factorization Machine]]: feature-based matrix factorization can be viewed as a restricted case of factorization machine to distinguish different types of features, allowing us to overcome some shortcomings of FM(e.g: including neighborhood information, exact implementation of rank,svd++ ). We call the model feature-based matrix factorization instead of restricted FM because the idea descends more naturally from matrix factorization.
 == Implementation ==

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-Feature-based matrix factorization is an abstract matrix factorization model that use features to describe the global bias and user/item factors.
+'''Feature-based matrix factorization''' is an abstract [[matrix factorization]] model that uses features to describe the global bias and user/item factors.
 The the model allows development of new model simply by feature defining. We can incorporate information such as temporal information, neighborhood information,
 taxonomy information into feature-based matrix factorization to make the model ''informative''.
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 without engineering efforts for writing codes for each new model.
+== Related Models ==
 * [[Factorization Machine]]: feature-based matrix factorization can be viewed as a restricted case of factorization machine to distinguish different types of features.
-= Implementation =
+== Implementation ==
 *[[SVDFeature]] is an efficient and scalable implementation of feature-based matrix factorization.
-= References =
+== References ==
 * [[User:Tqchen | Tianqi Chen]], Zhao Zheng, Qiuxia Lu and Yong Yu: Feature-based Matrix Factorization, http://arxiv.org/abs/1109.2271
 [[Category:Method]]

← Older revision		Revision as of 01:11, 24 September 2011
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	= References =		= References =
	* [[User:Tqchen \| Tianqi Chen]], Zhao Zheng, Qiuxia Lu and Yong Yu: Feature-based Matrix Factorization, http://arxiv.org/abs/1109.2271		* [[User:Tqchen \| Tianqi Chen]], Zhao Zheng, Qiuxia Lu and Yong Yu: Feature-based Matrix Factorization, http://arxiv.org/abs/1109.2271
		+	[[Category:Method]]