recommenderlab | Testing Recommender Algorithms - R package | Recommender System library

i'm current construct product recommendation using R recommenderlab, after compute AR recommender, i'm hoping to understand the association rules, but i couldn't found any why to extract the complete association rules from the recommender object.

Below is the sample dataset

I'm current running R-64bit (version 3.4.4) on windows machine with recommenderlab version 0.2-2

Below is the sample dataset

Want to save recommenderlab predict list as list of "" seperated list. I have one question in place for the same but here want to extend it with a twist.

I already tried few approaches and found below as relavent but stuck with a simple step of putting the ouptput in "" comma seperated script.

I have a recommendation problem that is fairly simple: I would like build a hybrid recommender with recommenderlab in R where I recommend already liked movies in the MovieLense data set together with popular movies and some random movies.

I am trying to work on a recommendation system in R. Data Set below: https://drive.google.com/file/d/1FVh-Xg3NBtzKgZHnDTi7IjaATW_fPmW9/view?usp=sharing

I have a matrix I have coerced from a realRatingMatrix in recommenderlab package in R. The data contains predictions of ratings between 0-1 for a number of products.

The matrix should contain customer numbers along the rows (row 2 down) so that column 1 header is row label, and product IDs along the columns in the first row from column 2 onwards. The problem I have is when I coerce to a matrix the data structure becomes messy:

EDIT: Link to Github repository www.github.com/APBuchanan/recommenderlab-model

I am trying to build a product recommendation model using binary data.

I have an issue with my code that I think stems from how the structure of my source data changes when I pass the function data.matrix=as(df,"binaryRatingMatrix")

My code is as follows:

So at the moment I am trying to figure out how to build a movie recommender system from MovieLense (https://grouplens.org/datasets/movielens/100k/). I read some instructions from a tutorial.

I am trying to test a binary recommendation systems I created with the recommenderlab package. When I run the calcPredictionAccuracy function I get the following error:

Error in .local(x, data, ...) : You need to specify how many items were given for the prediction! I have performed numerous searches and can't seem to find any solution to this issue. If I try to add the given argument the error changes to:

error.ubcf<-calcPredictionAccuracy(p.ubcf, getData(test_index, "unknown", given=3)) Error in .local(x, ...) : unused argument (given = 3)

Here is a quick look at my code:

my data set is binary.watch.ratings