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This presentation is about why initialization of matrix factorization methods is important and proposes an interesting initialization method (coined SimFactor). The method revolves around a similarity preserving dimensionality reduction technique. Context-based initialization is introduced as well.
As most of my recommender systems related research, this presentation focuses on implicit feedback (the case where user preferences are not coded explicitely in the data).
Originally presented at the 2nd workshop on Context-awareness in Retrieval and Recommendations (CaRR 2012) in Lisbon.