I am building a mobile app that can predict what apps users may be interested in downloading from the play store, based on what apps the user has already installed on their device and how much time they have spent on these apps. Also, there is the option to scroll through the top apps in the play store and you can "favourite" any apps that you find interesting/want to download.
Based on this data, I would like to make predictions and notify users of any new popular apps that are released in the future, however since the predictions will be user specific, I am unsure if the dataset will be too small for k-means clustering (Group the apps into genres and find most popular genres). 35 is the average number of apps installed on user's smartphones, and if you include any "favourited apps" the total dataset could be around 50.
Perhaps there is another more suitable technique I could apply to make accurate predictions but I am unsure where to go at the moment.