I have a set of user transactions with a service and I suspect that there is a strong correlation between how users rate the service and how likely they are to use the service again.
I have chosen to represent user ratings - for each user in the dataset - as a number [1-5] which is the average of all ratings the given user has given in the past. There is more to it in regards to how to represent trends etc, but this is not in context for the problem at hand.
My issue is that there is a high number of users that have never left any ratings at all and I am not sure how to deal with these users, in terms of finding the right value for the rating-related features for them.
What I have tried so far is to represent ratings in a different way, shown below:
UID rating_high rating_medium rating_low A 2 0 1 B 0 0 0
where each of the predictors represent the count of ratings - in each rating category - by each of the users in the dataset.
In the above case,
user A has left a good rating in 2 occasions, a low rating in one occasion and has never left a 'medium' rating. User B has never left any ratings for the service at all, therefore, he is assigned a count of
0 for all 'rating categories'.
on the 1-5 rating scale, I class all ratings below
rating_low, all those equal to
rating_medium and the ones above
I have not managed to find any other way to represent this data, but I would very much rather not drop this part of the dataset as I believe it caries valuable semantics for my problem.
Any ideas on how to best deal with this issue are most appreciated!