In performing ALS and getting an item matrix of latent features, what would be the best method for inferring the possible "meaning" of each latent factor in the item space? And as a corollary, are the any methods for successfully visualizing this data to gain some intuition or understanding?
Depending on the item domain You can expect different results. I would try to cluster data using cosinus similarity over item latent features. If number of items is large try DIMSUM (it's already implemented in Spark).
For the visualization You can try one of the available implementation of t-SNE. Quick links: wiki and YT - Google Talk about t-SNE Visualization. But here I cannot give You any more advice, because I have never tried to visualize it. I looking forward You will share Yours results.