01-09-2017 07:44 AM
Download the Recommender Extension here: https://marketplace.rapidminer.com/UpdateServer/faces/product_details.xhtml?productId=rmx_irbrecomme...
Then visit http://elico.rapid-i.com/recommender-extension.html and find they're sample workflows.
01-09-2017 08:31 AM
Thank you Thomas, but out instructor didnot provide information about this extension that you have mentioned. Is it possible to make a recommendation workflow without the extension ? if so what would be the difference in it ?
01-09-2017 08:42 AM
The Recommender Extension has a few more algorithms that are not native to the RapidMiner platform. I'm not sure if you can build a recommendation engine without the extension. I've never built one.
01-09-2017 08:52 AM
thank you very much for the replies and i am sorry to bug you like this but this means a lot to me. let me clarify the task a bit more and if you can please enlighten me with some info on how to do it. this is the task that has been given to us,
Develop a recommender system for a movie streaming service.
Using Rapid Miner you need to create and test a recommendation system that is as accurate as possible
we have been provided with 4 data sets (.CSV Files) which are LINKS, MOVIES, TAGS and RATINGS. they all have thounsands of entries and i am a little confused on how to work with this.
suggestive readings are,
Constructing recommender systems workflow templates in RapidMiner
A Decision Tree Based Recommender System
Extending RapidMiner with recommender systems algorithms
your help will be greatly appreciated sir !
01-09-2017 08:58 AM
"Extending RapidMiner with recommender systems algorithms" and "Constructing recommender systems workflow templates in RapidMiner"
I read that to mean the Recommender Extension and sample workflows, as I proposed above.
01-13-2017 08:58 AM
Also, the recommender system extension is free, so there really isn't a reason not to try it. It sounds like you are going to have to do some preliminary data ETL to combine the information from your 4 separate files into a single dataset and then use that dataset with the recommender operators to create a recommender system, along the lines described in the resources that Tom has suggested.