Crowdfunding is the talk of the town in recent years, especially among entrepreneurial circles. There are hundreds of crowdfunding sites out there, with AngelList and Kickstarter being two leading examples. Beyond equity and donation-based sites there are new twists such as RealCrowd, which focuses on real estate crowdfunding. Some other noteworthy crowdfunding sites include Seedrs, Crowdcube, Wefunder, FundersClub, Indiegogo, Quirky, Tilt, CircleUp, SeedInvest, GoFundMe and there are many many more!
These crowdfunding sites play the matchmaking role in bringing together relevant investors and startups, or backers and projects. It is important, therefore, for these crowdfunding platforms to find ways to give relevant recommendations to their users. Kickstarter, for instance, suggests projects to backers based on their location and also lists the most popular projects across different categories (Games, Design, Technology, etc).
PredictionIO can make these recommendation platforms more personalized for each user. Let’s focus on the investors (or backers) as users for our example. PredictionIO can help these users in the following ways:
By suggesting projects to users based on their previous investments or donations (“People who fund this project also fund…”).
By recommending projects to users based on the projects they have browsed (“People who view this project also view…”).
By giving personalized suggestions of, say, the top ten projects, based on each user’s browsing and funding history.
Below is a demonstration of how these features can be easily implemented with PredictionIO.
Feature 1: “People who fund this project also fund…”
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. The Python SDK is used in this example. Please follow the instructions in that page to install the SDK.
Step 3: Integrate PredictionIO
Follow the instructions in https://docs.prediction.io/templates/similarproduct/quickstart/ to integrate PredictionIO to your application’s data collection, and prediction querying stages.
Feature 2: “People who view this project also view…”
Though similar to the previous example, recommendation this time is given based on users’ browsing history and not their funding history.
Create another Similar Product Engine, and modify it to ignores all actions except the “view” actions. Now you can recommend similar projects to your users based on browsing history!
Feature 3. Top 10 Personalized Recommendation for each user
In addition to recommending similar projects, you can personalize the recommendation based on both the individual browsing and funding history of users.
You need a Recommendation Engine for this personalized recommendation feature. Since you have already integrated the SDK for importing data as described above, all you need to do now is to add a new Recommendation Engine.
This time, you don’t need to modify the algorithm settings to ignore some actions, because you want to provide personalized recommendation based on both browsing and funding history. Just follow this quickstart.
That’s it! This is how you can provide personalized recommendation to each investor based on their browsing and funding history. To make your own crowdfunding application smarter, try out PredictionIO today!