# Alternating Least Squares (ALS) - Why two different kinds of setting for $\lambda$ and $\alpha$?

I am trying to use Spark ALS to do recommendation with implicit feedback. However, I found there are two totally different kinds of settings available:

The first one is the setting used by the original paper: Collaborative Filtering for Implicit Feedback Datasets. They use $\lambda={\sim}150$ and $\alpha={\sim}40$

The other is the setting suggested by Sean Owen in this discussion, which uses $\lambda=1$ and $\alpha=40$.

Through I don't very clearly understand the underlying mechanism of implicit feedback recommendation. I have to admit that the recommendations made from Owen's setting is much better than those from the original setting.

Can someone explain what makes this difference?

The term $\lambda$ is used for regularization here and $\alpha$ is a constant controlling the rate of increase of the confidence matrix $C_{ui}$. The confidence matrix is your confidence in observing a particular preference a user, $u$, has on an item, $i$.
Remember that regularization is to help reduce overfitting by penalizing $X_u$ and $Y_i$ when minimizing the overall loss function.