Regarding the DBM - I also tried to use more than one layer, and without success. I tried out 3-layer and 4-layer autoencoders (can be called 1.5-layer and 2-layer DBM), with initialization by stacked RBMs or without it. It did not work well probably because: a) the model was inaccurate, and b) the learning method proposed for DBM was not completely correct. Intuitively, the right DBM-like model with the right learning method should have a chance to improve something on the Netflix task.
I found some improvement though (rather learning time than accuracy) in the standard RBMs. Instead of using CD, I split the weights into two sets, creating a directed RBM version. The "up" weights from the visible nodes to hidden are learned with CD with T=1. The "down" weights are learned to best fit the visible nodes, using the hidden nodes as predictors. The hidden nodes generated by CD T=1 are good enough, and we do not need additional iterations with increased T.
I also don't recall anyone successfully incorporating the date of the rating into the RBM. Mostly this was useful in other models because on any particular day people would just bias their ratings up or down a bit. But also, as one can imagine, over the course of a year or two their tastes would change. Is it straightforward to include that time dimension into RBMs, and if so, is that a recently discovered technique?