Item Based Collaborative Filtering keeps on running without end in recommenderlab r

Question

I have a sparse matrix sparse with 1100 columns corresponding to products and over 130k rows corresponding to users. The values are 1 or NA in this sparse matrix where 1 corresponds to a 'purchase' and NA to 'no purchase'.

I converted this matrix into a realRatingMatrix and tried to build an IBCF model in this way:

item_rec <- Recommender(sparse[1:nrow(sparse)],method="IBCF", 
                        param=list(method="Cosine",k=30, normalize_sim_matrix = F,
                                   alpha = 0.5, na_as_zero = F))

This command has been running for several hours now while another model with UBCF method was completed in seconds:

rec <- Recommender(sparse[1:nrow(sparse)],method="UBCF", 
                   param=list(method="Cosine",nn=5, minRating=1))

While the item-item similarity matrix may take more time to construct, is it normal for it to take this much time or am I doing something wrong with the code or my approach (like, should I convert it into a binaryRatingMatrix or use different arguments?)

Answer 1

Cosine distance is for now calculated on a dense matrix (a sparse implementation will hopefully be added someday). I think creating the full matrix and then calculating the distance takes so long.

Have you tries a smaller sample? Maybe 1000 or 10000 rows?