Classification Based Collaborative Filtering Model I was going through algorithms for collaborative filtering-based prediction. Most of the places, I read about using matrix factorization based on ratings of the likeness of the user. But for my use case, I have to predict on the bases of classified data. For example, I have a list of user who orders from place1,place2, etc and they mostly get their items delivered to address1,address2 etc. Initially, I thought to give binary ratings to the places, but I am having multiple criteria i.e addresses and places. How can I use collaborative filtering in this case?




User
Place
Delivery Address




One
X
A


One
X
A


One
Y
C


Two
Y
C


Two
Z
B


Three
Y
??
Actually, your objective here is not clear.
Following your example, I belive you want to obtain likeness/relationship between users and places.
So basically what you would want to do is to create a domain of users and places. Now for different addresses (eg. A and C), you can treat the same user (eg. One) as different users i.e. &quot;One-A&quot; and &quot;One-C&quot;. You can now assign a binary value for each &quot;user-address&quot; v/s &quot;place&quot;. Then you can use any collaborative filtering method to get the affinity of the users corresponding to a particular address, for a particular ordering place.