There are a number of collaborative filtering methods you can research. As an alternative, I'm going to suggest something very pedestrian instead that should be easy to code and should give you reasonable results. Caveat: This is just off the top of my head, and may be very inferior to some of the more established methods out there, but it seems like a fun topic so I'll take a shot.
Since the lists are not ranked, you can compare each list to each other list. The number of common albums should be easily determined (assuming album names are not misspeled). Use that as a weight for each other album on everyone else's lists. So each album you don't have that appears on the list of someone with whom you share at least one album in common will appear in your recommendation list, weighted by the albums you have in common with that user. You will then aggregate the scores for each album across all other users.
So if you have 5 albums in common with User B, you would assign a weight of 5 to each album on that user's list that isn't on yours. Moving to User C, you would do the same. If User C has 3 common albums with you, then any albums on both User B and User C's list that aren't on yours now have a weight of 8.
You may want to increase the weight for lists where you share a lot of albums so those recommendations are even more valuable. In other words, a list with 10 common albums might get a weight of higher than 10, so it would count for more than 10 lists where you share a single album. This should be easy enough to tweak and test.
Once you have run through every other list, you can truncate your recommendation list to top 10 or something reasonable.