Latest Steam Labs experiment The Steam Interactive Recommender is now live

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8.44 user review score. Average score out of 10, based on 130 review scores” style=”background: #4db53c”>
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You know how we all have that issue? When we’re sitting on the couch, scrolling through Netflix for something to watch, and the next thing you know it’s too late because you’ve been too busy scrolling? Well, what if it was video games instead? Introducing the latest Steam Labs experiment to go live… The Steam Interactive Recommender.

What started as the second Steam Labs experiment, Interactive Recommender is now live on the Steam homepage. It is the latest addition to some of the other Discovery features on Steam that allows players to connect to the games that they might be interested in. There are literally tonnes of video games out there, and it’s sometimes hard to find something new that you like when you’re looking for it, these features aim to get rid of that.

It’s a pretty nifty tool for those who struggle to find their next game to play (and somehow don’t have a huge backlog of games). Utilising Machine Learning, the interactive recommender is designed to pair users with the next best game to play, plus (as the name suggests) it is completely interactive and customisable.

Essentially what it does is track the playtime of Steam users, then if someone is out there playing the same sort of games as you, and they’re playing a game which you don’t have, then that game might be a good fit for you. You might even see a “Players like you love this game” tag on some game pages now, indicating this exact process.

You can customise the Interactive Recommender straight from the homepage by highlighting ‘Your Store’ and clicking ‘Interactive Recommender’. There are several options you can customise, including how popular or niche a game is, how older or newer it is, and even including or excluding certain game tags.

What do you think of the Steam Interactive Recommender? Do you think it’s useful? Will you be using it? Or have you got too much of a backlog to even think about using it? Let us know!

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