Returns all items that have not been rated by the user (3) and that were preferred by another user (2) that has preferred at least one item (1) that the current user has preferred too.

This strategy uses sampling to limit the number of items that are considered, by sampling three different things, noted above:

1. The items that the user has preferred
2. The users who also prefer each of those items
3. The items those users also prefer

There is a maximum associated with each of these three things; if the number of items or users exceeds that max, it is sampled so that the expected number of items or users actually used in that part of the computation is equal to the max.

Three arguments control these three maxima. Each is a "factor" f, which establishes the max at f * log2(n), where n is the number of users or items in the data. For example if factor #2 is 5, which controls the number of users sampled per item, then 5 * log2(# users) is the maximum for this part of the computation.

Each can be set to not do any limiting with value NO_LIMIT_FACTOR.