Hi,
I am looking into designing implementing a recommendation engine with the
below use cases . There is no specific rating's etc given by user's as such
for items accessed.
1. Item's viewed by other user's who viewed this particular Item
2. Item's booked by other user's who viewed this particular Item
3. Most viewed item('s) viewed by other user's who viewed this particular
Item
The idea behind is the below :
1.I want to interpret user behavior where recommendation would be based on
the other user's patterns which falls into the bracket of CF(item based
similarities or user based) .
2.I want to exploit item item similarity which is based on N number of
attributes. The attributes can be say : price,location,features(1...n) as
so on.
The recommendation should be a mix of both of the above.
A) For 1 given that I don't have an explicit rating my initial thought was
around interpreting ratings as based on what user does for a product eg
If he only views it I give a 1 rating
If he further sees the details I give 2 rating
If he goes to the booking page I give him 3 rating
If he books it I give him 4 rating etc
And when I have the same I would go for a standard CF item-item similarity
implemented through Mahout
B) For 2. I was looking into our search framework(Solr) to give the same
i.e Solr's MoreLikeThis feature. Also carrot also seems to make it better
but I don't how much would that be scalable etc.
Idea is to get an intersection if A and B to get started with. Also I need
to figure out the processing and latency part of getting the results as
well.
I guess the group user's must have solved a similar problem more
efficiently and could advise better.
Please let me know the same.
Regards,
Shubham
