In this era of platforms, app stores are essential and the competition in the market is fierce. As the hegemony of Apple App Store, Google Play, Facebook App Center and Amazon Appstore continues, competitors are hoping to take a slice of the app store pie.
Some examples include Appolicious, Appreciate, Getjar, Appoke, and Firefox Marketplace. There are also app stores that focus on specific verticals like games, these are becoming more and more popular, with Games Grabr, Clay.io and GameFinder being some examples.
While the app store business can be profitable, the fact that there are so many apps out there poses a discovery problem. As numerous new apps come out every day with some being junk and some relevant only to a small group of users, app stores need to know how to recommend the most relevant apps to their users in order to remain popular.
For this reason, most app stores offer personalized recommendations to their users.
You may be wondering how such personalized discovery features can be built. The truth is, with the help of PredictionIO, the open source Machine Learning server, it can be built in no time! In this tutorial, we are going to show you how:
Discovery based on Recent Interests
On the front page, you can make serendipitous recommendations to help users discover new and interesting apps based on what they have recently browsed. This can be achieved using an Item Similarity Engine.
Step 1: Install PredictionIO Server
Follow the instructions to install PredictionIO.
Step 2: Install the SDK in Your Development Language
PredictionIO provides SDK in various languages. PHP is used in this example. Please follow instructions here.
Step 3: Integrate PredictionIO
Follow the instructions in http://docs.prediction.io/templates/similarproduct/quickstart/ to integrate PredictionIO to your application’s data collection, and prediction querying stages.
Personalized Recommendation for Each App Category
Consider recommending 10 apps in each category to users based on a) their previous downloads; b) the star ratings they gave to apps previously, it is very easy to do with PredictionIO by following the instructions in http://docs.prediction.io/templates/recommendation/quickstart/.
Without a doubt, personalization can greatly increase the effectiveness of app recommendations and is helpful in retaining customers. This tutorial has demonstrated how to quickly embed personalization features in different ways for app stores, and hopefully this is useful for some of you guys! For more details on how PredictionIO can be helpful for building smarter software using Machine Learning, visit our website at http://prediction.io.