Description Usage Arguments Details Author(s) Examples
Finds "representative" users, i.e. the users whose ratings correlate the highest with the average ratings of the items they rate.
1 | focusGrp(ydotsObj, ratingsIn, k = 10, minn = 50)
|
ydotsObj |
Object of type |
ratingsIn |
Input data frame, training set. Within-row format is (UserID, ItemID, rating). |
k |
Desired size of the focus group. |
minn |
Minimum number of ratings for a user to be considered for the group. |
For each user i, vectors u and v will be formed; u will be the vector of ratings set by user i; For v[j], the code will find the item ID m of the j-th component of u, then set v[j] to the average rating of all users for item ID m. Then the mean abolute error will be computed, using u to predict v; this will be done for each user, and the k users with the lowest MAEs will be chosen.
Pooja Rajkumar and Norm Matloff
1 2 3 4 5 6 | ivl <- InstEval
ivl$s <- as.numeric(ivl$s)
ivl$d <- as.numeric(ivl$d)
ivl3 <- ivl[,c(1,2,7)]
ydo <- findYdotsMM(ivl3)
focusGrp(ydo,ivl3) # users 90, 118, 231, ... are chosen
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