myExperiment - Workflows

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Ninoaf

Item recommendation collaborative-based wo... (1)

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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

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Ninoaf

SVD user-based collaborative filtering rec... (1)

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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

Attributions: User-based collaborative filtering recommender system template Datasets for the pack: RCOMM2011 recommender systems workflow templates

RapidMiner

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Ninoaf

LSI content based recommender system template (1)

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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 ...