JohnLangford/vowpal_wabbit — pull request #117 by petricek (2013-04-05 07:47:00+00:00)
Title: 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
# already seen user-item pairs that should not be recommended
# users to generate recommendations for
# items to consider for recommending
# how many items to recommend per user
# 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