There's evolutionary algorithm in SGD to find those in adaptive way using cross-validation but it may be too demanding in terms of # of experiments. just FYI
On Mon, Dec 20, 2010 at 11:58 PM, Sebastian Schelter < [email protected]> wrote: > Hi Dmitriy, > > the paper states that it's easy to find a good lambda value with 3-4 > experiments. I still have to verify that assumption on a real dataset. > > --sebastian > > > On 21.12.2010 00:57, Dmitriy Lyubimov wrote: > >> HI Sebastian, >> >> how do you come up with a good Lambda to use with this weighted ALS? >> >> On Mon, Dec 20, 2010 at 3:27 PM, Sebastian Schelter (JIRA) >> <[email protected]>wrote: >> >> [ >>> >>> https://issues.apache.org/jira/browse/MAHOUT-542?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel >>> ] >>> >>> Sebastian Schelter updated MAHOUT-542: >>> -------------------------------------- >>> >>> Attachment: MAHOUT-542-2.patch >>> >>> An updated version of the patch. I fixed a small bug, added more tests >>> and >>> polished the code a little. >>> >>> The distributed matrix factorization works fine now on a toy example. The >>> next steps will be to use real data and do some holdout tests. >>> >>> MapReduce implementation of ALS-WR >>>> ---------------------------------- >>>> >>>> Key: MAHOUT-542 >>>> URL: https://issues.apache.org/jira/browse/MAHOUT-542 >>>> Project: Mahout >>>> Issue Type: New Feature >>>> Components: Collaborative Filtering >>>> Affects Versions: 0.5 >>>> Reporter: Sebastian Schelter >>>> Attachments: MAHOUT-452.patch, MAHOUT-542-2.patch >>>> >>>> >>>> As Mahout is currently lacking a distributed collaborative filtering >>>> >>> algorithm that uses matrix factorization, I spent some time reading >>> through >>> a couple of the Netflix papers and stumbled upon the "Large-scale >>> Parallel >>> Collaborative Filtering for the Netflix Prize" available at >>> >>> http://www.hpl.hp.com/personal/Robert_Schreiber/papers/2008%20AAIM%20Netflix/netflix_aaim08(submitted).pdf >>> < >>> http://www.hpl.hp.com/personal/Robert_Schreiber/papers/2008%20AAIM%20Netflix/netflix_aaim08%28submitted%29.pdf> >>> >>> >>> . >>> >>>> It describes a parallel algorithm that uses "Alternating-Least-Squares >>>> >>> with Weighted-λ-Regularization" to factorize the preference-matrix and >>> gives >>> some insights on how the authors distributed the computation using >>> Matlab. >>> >>>> It seemed to me that this approach could also easily be parallelized >>>> >>> using Map/Reduce, so I sat down and created a prototype version. I'm not >>> really sure I got the mathematical details correct (they need some >>> optimization anyway), but I wanna put up my prototype implementation here >>> per Yonik's law of patches. >>> >>>> Maybe someone has the time and motivation to work a little on this with >>>> >>> me. It would be great if someone could validate the approach taken (I'm >>> willing to help as the code might not be intuitive to read) and could try >>> to >>> factorize some test data and give feedback then. >>> >>> -- >>> This message is automatically generated by JIRA. >>> - >>> You can reply to this email to add a comment to the issue online. >>> >>> >>> >
