topN recommendation

Hi John,

I have implemented a topN recommender using the the library interface.

Product of users and items is scored and ```topN``` for each user is printed out.
 
```
$ ./recommend
usage: ./recommend 
         [-b <2|4|8|...|32>]  # blacklist bloom filter size is 2^b
         [-v]                       # print bloom filter usage
         <blacklist>            # already seen user-item pairs that should not be recommended
         <users>                # users to generate recommendations for
         <items>                # items to consider for recommending
         <topN>                 # how many items to recommend per user
         <vwparams>         # command line params for vw (-i model ...)
```
I am using it with a matrix factorization model. It uses a simple bloom filter to not recommend already seen items. 

Would it make more sense to make it a reduction? I should probably use murmurhash like vw for the bloom filter?

V