Valve offers a look at what they're cooking up by opening up the doors to the Steam Labs. This unveils "experiments," works in progress which will not necessarily succeed in the long run. The first three of these are: Micro Trailers, the Interactive Recommender, and Automatic Show. Micro Trailers "are six-second looping videos designed to quickly inform viewers about titles on Steam with a presentation that's easy to skim." The Automatic Show is a bot-generated entertainment program. Finally the Interactive Recommender is a new recommendation engine. This last one seems to hold the most promise, as they offer a separate news post with extensive details on its operation. Here's an explanation on how they hope this might improve upon previous ways of generating game recommendations:

Recommendations on Steam
Rather than introducing a big change to the way customized recommendations are determined on Steam, we’re introducing this new recommender as an experiment customers can seek out and try. This will help us get better usage data while avoiding any sudden shifts that we know can be frustrating for customers and developers who are accustomed to Steam. Should the interactive recommender or related experiments prove useful, we’ll share an update before rolling out any big changes to the way Steam recommends titles to people. The data driving Popular, New, and Trending is different from that of the Discovery Queue, this new recommender, and so on. We view this new interactive recommender as one discovery element among many, and look forward to introducing more ways to connect customers with interesting content and developers.

Recommendations and new games
New games in a system such as this one have a chicken-and-egg effect known as the "cold start" problem. The model can't recommend games that don't have players yet, because it has no data about them. It can react quite quickly, and when re-trained it picks up on new releases with just a few days of data. That said, it can't fill the role played by the Discovery Queue in surfacing brand new content, and so we view this tool to be additive to existing mechanisms rather than a replacement for them.

Why it works for short games
The recommender knows that there are great short-form games you can finish in an hour, and those you'll play for thousands. Your playtime data is normalized to reflect the distribution of playtime in each game, ensuring that all games are on an equal footing.

No need for developer optimization
Sometimes, computer-driven discovery makes creators focus on optimizing for "The Algorithm" rather than customers. You might ask, how is this any different? We designed the recommender to be driven by what players do, not by extrinsic elements like tags or reviews. The best way for a developer to optimize for this model is to make a game that people enjoy playing. While it's important to supply users with useful information about your game on its store page, you shouldn't agonize about whether tags or other metadata will affect how a recommendations model sees your game.