Abstract With the rapid development of database technologies, multiple data sources may be available for a given learning task (e.g. collaborative filtering). However, the data sources may contain different types of features. For example, users' profiles can be used to build recommendation systems. In addition, a model can also use users' historical behaviors and social networks to infer users' interests on related products. We argue that it is desirable to collectively use any available multip.
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