Pack: Recommender system engine in RapidAnalytics

Description:

 This is the dataset that is used by process:

http://www.myexperiment.org/workflows/2901.html

Description:

 Front-end recommendation web service has a simple job to query the cached recommendations from the \textit{item recommendation table}

Description:

No description available

Description:

  Periodically we have to do a full re-training on whole train set by the offline update recommendation web service.

Description:

 After some arbitrary number of recommendations to specific users, system has to update recommendations in \textit{item recommendation table}. This is accomplished by calling the online update recommendation web service, which updates the recommendation model in RapidAnalytics repository and updates the recommendations for specific users in \textit{item recommendation table}.

Description:

 After some arbitrary number of recommendations to specific users, system has to update recommendations in item recommendation table. This is accomplished by calling the online update recommendation web service, which updates the recommendation model in RapidAnalytics repository and updates the recommendations for specific users in item recommendation table.

Description:

 This workflow takes macro value of user request and computes top-n recommendations from already learned model on train set. Macro value is transformed to example set with

the appropriate \textit{user identification} role within \textit{ProcessInput} subprocess operator.

Simple recommendation web service workflow takes three inputs:

\begin{enumerate}

\item Macro value \textit{user} - identification number of user request

\item Train set from RM repository

\item Learned Model from RM repository

\end{enumerate}

Description:

No description available

Description:

 When the specific user $i$ "consumes" certain item $j$ the write activity web service is called, which writes activity $(i,j)$ to \textit{train set table} and removes recommendation $j$ for user $i$ from \textit{item recommendation table} in SQL database.