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I would like to develop a weighted hybrid recommendation system from multiple data sources.

Given are:
1. Explicit feedback: on different products in the range of 0 to 10
(0 means no feedback exists here)

Implicite feedback:
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?

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Take a look at Hybrid Recommender Systems: Survey and Experiments, specifically table 3 has a list of approaches for combining different kinds of data sources.

Regarding your first question, you can scale the different metrics to lie in the same range (for eg. between 0 and 1). A popular scaling approach to compute the scaled rating $R_{scaled}$ between some $a$ and $b$ is: $R_{scaled} = \frac{(b - a)(R - min)}{max - min}$, where $min$ and $max$ are the minimum and maximum of the original rating scale.


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