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Cosine Distance Recommendation / Collaborative Filtering Example
# lets make some dummy data
n_rows <- 1000
n_cols <- 100
mat <- matrix(0, nrow=n_rows, ncol=n_cols)
mat <- apply(mat, 2, function(x) { return(rbinom(n_rows, size=1, prob=0.1))})
colnames(mat) <- paste0("event", 1:n_cols)
rownames(mat) <- paste0("pol", 1:n_rows)
# lets take a look at it before we do some math
head(mat)
# now we're going to write a script that,
# given a vector of events a politicians has gone to
# will return the cosine similarity of all other
# politicians. This could easily be adapted to search across
# ALL politicians, so that we could identify the nearest X
# neighbors of each politician
# I got this code from here:
# http://www2.research.att.com/~volinsky/DataMining/Columbia2011/HW/HW6-Solution.pdf
# lets take one pol for starters
sample_pol <- "pol100"
sample_pol_vec = mat[samp,]
# now we're going to compute cosine similarity in three steps
mat_fcn <- function(pol_x){
out <- sum(pol_x * sample_pol_vec)
return(out)
}
numerator <- apply(mat, 1, mat_fcn)
denominator <- sqrt(sum(sample_pol_vec ^ 2) * rowSums(mat * mat))
cosine_distance <- numerator / denominator
# We then order the other pols by their similiarity with our sample_pol,
# keeping the top 10 (not including our sample_pol!).
cosine_order <- order(cosine_distance, decreasing=T)[2:11]
nearest_10_neighbors <- names(cosine_distance[cosine_order])
# The nearest 10 neighbors are:
print(nearest_10_neighbors)
# The 10 events these nearest 10 have gone to the most are:
nearest_10_neighbor_events <- colSums(mat[nearest_10_neighbors,])
print(sort(nearest_10_neighbor_events, decreasing=TRUE)[1:10])
# The Cosine Distance betwen our sample_pol and her nearest neighbor is:
print(cosine_distance[cosine_order][1])
# The events that both these two pols have gone to is
nearest_pol <- names(cosine_distance[cosine_order][1])
events <- colnames(mat)
nearest_pol_events <- events[mat[nearest_pol,]==1]
sample_pol_events <- events[mat[sample_pol,]==1]
shared_events <- sample_pol_events[sample_pol_events %in% nearest_pol_events]
print(shared_events)
@merveydn

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commented Aug 20, 2018

The events that the sample pol has attended are not removed, I think?

@merveydn

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commented Aug 20, 2018

Trying to write a wrapper function using your code , can you please help me to remove the events? Tried doing this but did not work.

recomender = function(df, userid){

sample_user <- c("userid")
sample_user_vec = df[sample_user,]
mat_fcn <- function(df){
out <- sum(df * sample_user_vec)
return(out)
}

numerator <- apply(df, 1, mat_fcn)
denominator <- sqrt(sum(sample_user_vec ^ 2) * rowSums(df * df))
cosine_distance <- numerator / denominator

cosine_order <- order(cosine_distance, decreasing=T)[-1]
nearest_neighbors <- names(cosine_distance[cosine_order])

nearest_neighbor_events <- colSums(df[nearest_neighbors,])
sort(nearest_neighbor_events[df[sample_user, ] == 0], decreasing=TRUE)}

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