# User: Ninoaf

 Name: Ninoaf Joined: Monday 10 May 2010 16:56:35 (UTC) Last seen: Tuesday 30 July 2013 20:32:02 (UTC) Email (public): nino.antulov [at] irb.hr Location: Zagreb, Croatia Ninoaf has been credited 11 times Ninoaf has an average rating of: 0.0 / 5 (0 ratings in total) for their items

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Field/Industry: Computer science

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Rudjer Boskovic Institute ,
Zagreb, Croatia

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Created: 2012-06-20

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.

Created: 2012-06-20 | Last updated: 2012-06-20

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

Created: 2012-06-20 | Last updated: 2012-06-20

This workflow takes conent data from VideoLectures.Net Recommender System Challenge and extractes word-vectors for each lecture. Latent semantic analysis with Singular Value Decomposition is done on item-word binary matrix. The last step is the binomializaiton of dataset.

Created: 2012-06-03

No description

Created: 2012-05-11

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}.

Created: 2012-05-11 | Last updated: 2012-05-11

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.

Created: 2012-05-11 | Last updated: 2012-05-11

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

Created: 2012-05-11 | Last updated: 2012-05-11

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 Mo...

Created: 2012-05-10 | Last updated: 2012-05-10

This workflow provides transformation of an item description attribute set from RMonto operator into a format required by attribute based k-NN operators of the Recommender extension.

Created: 2012-03-26

This workflow takes input 2 as as train set for several item recommendation predictions. We build four different recommendation model which are combined into one model with operator Model Combiner. Then we take input 1 as a test set and apply merged model. Train and test set must contain user_id, item_id and rating attributes which need to have special roles user identification, item identification and label. This workflow uses recommender system extension.

Created: 2012-01-19

This workflow takes input 2 as a train set for recommender systems. We build two item recommendation models: item k-NN (collaborative based) and item attribute k-NN (content based). Item attribute k-NN operator takes additional item attributes from input 3. We combine two models with operator model combiner and test performance on test set (input 1). Train and test set must contain user_id and item_id attributes which need to have special roles user identification and item identification. Th...

Created: 2012-01-19

This workflow takes input 2 as a train set for item-k-NN recommender system. The model peformance is evaluated on test set (input 1). This workflow uses recommender system extension. Train and test set must contain user_id and item_id atrributes which need to have special roles user identification and item identification.

Created: 2012-01-09 | Last updated: 2012-01-09

This workflow takes user-item matrix A as a input. Then it calculates reduced SVD decomposition A_k by taking only k greatest singular values and corresponding singular vectors. This worfkflow calculates recommendations and predictions for particular user %{id} from matrix A. Particular row %{id} is taken from original matrix A and replaced with %{id} row in A_k matrix. Predictions are made for %{id} user based on another users A_k. Note: This workflow uses R-script operator with R library ...

Created: 2011-05-09 | Last updated: 2011-05-09

Credits: Ninoaf Matko BoÅ¡njak

This workflow performs LSI text-mining content based recommendation. We use SVD to capture latent semantics between items and words and to obtain low-dimensional representation of items. Latent Semantic Indexing (LSI) takes k greatest singular values and left and right singular vectors to obtain matrix  A_k=U_k * S_k * V_k^T. Items are represented as word-vectors in the original space, where each row in matrix A represents word-vector of particular item. Matrix U_k, on the other hand ...

Created: 2011-05-06 | Last updated: 2011-05-09

Credits: Ninoaf Matko BoÅ¡njak

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