Steam Personalized Its Game Recommendation Engine for Better Suggestions
Daniel Johnson — September 16, 2019 — Tech
References: steamcommunity & engadget
Valve has recently updated its game recommendation engine for Steam, in order to provide more accurate game suggestions. The new suggestions are the result of changes to the algorithm, bug fixing, and diversifying the tagging system. Users can access the new game recommendation engine by using either the "More Like This" feature or the "Recommended for You" feature on the steam platform.
Most notably, the platform will no longer place as much emphasis on recommending popular titles, this results in far more personalized recommendations. The changes were tested on five percent of Steam users, and the results were encouraging. Users in the test group were 15% more likely to select a game in the recommended section than users in the control group.
Image Credit: Steam
Most notably, the platform will no longer place as much emphasis on recommending popular titles, this results in far more personalized recommendations. The changes were tested on five percent of Steam users, and the results were encouraging. Users in the test group were 15% more likely to select a game in the recommended section than users in the control group.
Image Credit: Steam
Trend Themes
1. Personalized Game Recommendations - Gaming and media industry can explore AI algorithms to improve customer experience through personalized game recommendations.
2. Diversifying Tagging System for Better Suggestions - Software and technology companies can explore the use of smart tagging systems to improve product recommendations and search results on their platforms.
3. Reducing Emphasis on Popular Titles - Entertainment industry can explore reducing the emphasis on popular titles in their marketing and recommendation strategies to provide more diverse and meaningful content to their customers.
Industry Implications
1. Gaming - Gaming industry can use AI-based recommendation engines to improve customer retention and engagement.
2. Software and Technology - Technology companies can explore and implement smart algorithms in their product recommendation and search systems for better customer engagement.
3. Entertainment - Entertainment industry can use data-driven recommendations to diversify content and provide a personalized experience to their customers.
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