Hi All, Are there any (dis)advantages of using tri-factorization (||X - USV'||) as opposed to bi-factorization ((||X - UV'||)) for recommender systems? I have been reading a lot about tri-factorization and how they can be seen as co-clustering of rows and columns and was wondering if such as technique is implemented in Mahout?
Also, I am particularly interested in implicit-feedback datasets and the only MF approach I am aware of is the ALS-WR for implicit feedback data implemented in mahout. Are there any other MF techniques? If not, is it possible (and useful) to extend some tri-factorization to handle implicit-feedback along the lines of "Collaborative Filtering for Implicit Feedback Datasets" (the approach implemented in Mahout). I apologize for any inconvenience as this question is very general and might not be relevant to Mahout and I would really appreciate any thoughts/feedback. Thanks, Rohit
