Tag Results

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

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

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

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

Building a knowledge base for data mining ... (7)

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Scientific workflow for creating the knowledge base. 

Created: 2013-10-03 | Last updated: 2018-09-20

Credits: respinosa

Attributions: Measuring data quality criteria in arff files Data Mining Algorithms

User friendly data mining (7)

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This scientific workflow was designed for the novice data miners to learn how mining algorithms behave when applied on their datasets. The workflow is the responsible for invoking the mining recommender, as well as using the datasets required by the user, thus returning the ranking of algorithms according to the design criteria of the recommender. 

Created: 2013-10-03 | Last updated: 2019-01-08

Credits: respinosa