Key issues with recommendation systems
There are three key issues with recommender systems in general:
- Gathering known input data
- Predicting unknown from known ratings
- Evaluating Prediction methods
Gathering known input data
The first interim milestone in building a recommendation system is to gather the input data, that is, customers, products, and the relevant ratings. While you already have customers and products in your CRM and other systems, you would like to get the ratings of the products from the users. There are two methods to collect product ratings:
- Explicit: Explicit ratings means the users would explicitly rate a particular item, for example, a movie on Netflix, a book/product on Amazon, and so on. This is a very direct way to engage with users and it typically provides the highest quality data. In real life, despite the incentives given to rate an item, very few users actually leave ratings for the products. Getting explicit ratings is therefore not scalable for any meaningful prediction...
