Workflows

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Showing 42 results. Use the filters on the left and the search box below to refine the results.
Type: RapidMiner Tag: rapidminer

Workflow User-based collaborative filtering recomme... (1)

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The workflow for user-based collaborative filtering, takes only one example set as an input: a user-item matrix, where the attributes denote item IDs, and rows denote users. If a user i has rated an item j with a score s, the matrix will have the value s written in i-th row and j-th column. In the context of the process we define the ID of the user %{id}, desired number of recommendations %{recommendation_no}, and the number of neighbors used in ca...

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

Credits: User Matko Bošnjak User Ninoaf

Attributions: Blob Datasets for the pack: RCOMM2011 recommender systems workflow templates

Workflow Content based recommender system template (1)

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As an input, this workflow takes two distinct example sets: a complete set of items with IDs and appropriate textual attributes (item example set) and a set of IDs of items our user had interaction with (user example set). Also, a macro %{recommendation_no} is defined in the process context, as a required number of outputted recommendations. The first steps of the workflow are to preprocess those example sets; select only textual attributes of item example set, and set ID roles on both of th...

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

Credits: User Matko Bošnjak User Ninoaf

Attributions: Blob Datasets for the pack: RCOMM2011 recommender systems workflow templates

Workflow Collaborative filtering recommender (1)

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This process executes a collaborative filtering recommender based on user to item score matrix. This recommender predicts one user’s score on some of his non scored items based on similarity with other users. The inputs to the process are context defined macros: %{id} defines an item ID for which we would like to obtain recommendation and %{recommender_no} defines the required number of recommendations and %{number_of_neighbors} defines the number of the most similar users taken into a...

Created: 2011-03-15 | Last updated: 2012-03-06

Workflow Content based recommender (1)

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This process is a special case of the item to item similarity matrix based recommender where the item to item similarity is calculated as cosine similarity over TF-IDF word vectors obtained from the textual analysis over all the available textual data. The inputs to the process are context defined macros: %{id} defines an item ID for which we would like to obtain recommendation and %{recommender_no} defines the required number of recommendations. The process internally uses an example set of...

Created: 2011-03-15 | Last updated: 2011-03-15

Workflow Item to item similarity matrix -based reco... (1)

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This process executes the recommendation based on item to item similarity matrix. The inputs to the process are context defined macros: %{id} defines an item ID for which we would like to obtain recommendation and %{recommender_no} defines the required number of recommendations. The process internally uses an item to item similarity matrix written in pairwise form (id1, id2, similarity). The process essentially filters out appearances of the required ID in both of the columns of the pairwis...

Created: 2011-03-15 | Last updated: 2011-03-15

Workflow Random recommender (1)

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This process does a random item recommendation; for a given item ID, from the example set of items, it randomly recommends a desired number of items. The purpose of this workflow is to produce a random recommendation baseline for comparison with different recommendation solutions, on different retrieval measures. The inputs to the process are context defined macros: %{id} defines an item ID for which we would like to obtain recommendation and %{recommender_no} defines the required number of ...

Created: 2011-03-15 | Last updated: 2011-03-15

Workflow Change Class Distribution of Your Training... (1)

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This example process shows how to change the class distribution of your training data set (in this case the training data is what ever comes out of the "myData reader"). The given training set has a distribution of 10 "Iris-setosa" examples, 40 "Iris-versicolor" examples and 50 "Iris-virginica" examples. The aim is to get a data set which has the class distribution for the label, lets say 10 "Iris-setosa", 20 "Iris-versicolor" and 20 "Iris-virginica. Beware that this may change some propert...

Created: 2011-01-21 | Last updated: 2011-01-21

Workflow 2. Getting Started: Retrieve and Apply a M... (1)

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This getting started process demonstrates how to load (retrieve) a model from the repository and apply it to a data set. The result is a data set (at the lab output for "labeled data" ) with has a new "prediction" attribute which indicated the prediction for each example (ie. row/record). You will need to adjust the path of the retrieve data operator to the actual location where the model is stored by a previews execution of the "1. Getting Started: Learn and Store a...

Created: 2011-01-17 | Last updated: 2011-01-19

Workflow 1. Getting Started: Learn and Store a Model (1)

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This getting started process shows the first step of learning and storing a model. After a model is learned, you can load (Retrieve operator) the model and apply it to a test data set (see 2. Getting Started: Retrieve and Apply Model). The process is NOT concerned with evaluation of the model. This process will not immediately run in RapidMiner because you have to adjust the repository path in the Retrieve operator. Tags: Rapidminer, model, learn, learning, training, train, store, first step

Created: 2011-01-17 | Last updated: 2011-01-17

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Workflow Connect to twitter and analyze the key words (1)

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Hi All, This workflow connects RapidMiner to Twitter and downloads the timeline. It then creates a wordlist from the tweets and breaks them into key words that are mentioned in the tweets. You can then visualize the key words mentioned in the tweets. This workflow can be further modified to review various key events that have been talked about in the twitterland. Do let me know your feedback and feel free to ask me any questions that you may have. Shaily web: http://advanced-analyti...

Created: 2010-07-26 | Last updated: 2010-07-26

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