I would like to develop a weighted hybrid recommendation system from multiple data sources.
1. Explicit feedback: on different products in the range of 0 to 10
(0 means no feedback exists here)
2. Exact purchases data coded binary (0 means no purchase by userX on itemY)
-> very sparse 1% of users
3. Click data coded as integer from 0 to XX (means how often a user has clicked on a particular product page) -> sparse 10% of users
The first question is: How could I transform all the different feedback ranges to one comparable Rating-Matrix?
And the second question is:
How I could design the recommander?
My first idea was to calculate 3 different recommender systems based on every single matrix and than combine those in a hybrid system. But I don't know if this is a very useful way in terms of data source sparsity of matrix 2 and 3.
Is there any other idea?