A team from Google is sharing a multi-part series on their research blog about how they’re using these techniques to improve the effectiveness of the ‘You might also like’ and ‘Similar apps’ suggestions in the Google Play Store:
Compared to the control (where no re-ranking was done), we saw a 20% increase in the app install rate from the “You might also like” suggestions. This had no user perceivable change in latency.
Discovering these nuances requires both an understanding what an app does, and also the context of the app with respect to the user. For example, to an avid sci-fi gamer, similar game recommendations may be of interest, but if a user installs a fitness app, recommending a health recipe app may be more relevant than five more fitness apps. As users may be more interested in downloading an app or game that complements one they already have installed, we provide recommendations based on app relatedness with each other (“You might also like”), in addition to providing recommendations based on the topic associated with an app (“Similar apps”).
One particularly strong contextual signal is app relatedness, based on previous installs and search query clicks. As an example, a user who has searched for and plays a lot of graphics-heavy games likely has a preference for apps which are also graphically intense rather than apps with simpler graphics.
This switch to machine learning refinements is happening throughout Google’s product range at the moment and is leading to arguably the most significant changes in overall user experience – above and beyond any visual design advances they’ve introduced.
Part of Friday Inspirations, an ongoing MEX series exploring tangents and their relationship to better experience design. We explain the origins of the Inspirations series in this MEX podcast and article. Share your own inspirations on Twitter at #mexDTI.