Data Science Asked by 5im on March 28, 2021
I’m new in recommender systems and I try to find similar users of a base users for user-based collaborative filtering.
When I calculated the similarity score now between two users (based on there ratings with pearson algorithm [or resnick’s weighted pearson algorithm]) I get a similarity score from -1 to 1.
Is it a good idea to normalize this values to 0 to 1 (-1 would become 0 and 1 would be 1) to make it comparable to other algorithms?
In fact I try to build recommendations and with a negative similarity score of a user the calculated/predicted rating could be negative as well which make no sense.
Should I normalize/scale “-1 to 1” to “0 to 1” or cut off all users with similarity score below 0?
(maybe the question also could be: “Which users should be taken as mentor to recommend new items on a similarity score from -1 to 1? Or should I take the top n users with highest similarity score?”)
One thing you can do is to separate the contributions of:
Then you can:
Answered by Mirko Mazzoleni on March 28, 2021
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