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Matej MihelÄiÄ‡

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MovieLens data for hybrid recommenders

Created: 2012-05-17 11:04:57 | Last updated: 2012-05-17 11:06:10

Credits: tomS

License: Creative Commons Attribution-Share Alike 3.0 Unported License

This is MovieLens data set containing user,item, ratings and item attributes enabling hybrid recommendation system testing.

File type: Trident (Package)

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Matej MihelÄiÄ‡

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Hybrid recommender RapidMiner and extension operators

Created: 2012-05-17 10:57:03 | Last updated: 2012-05-17 11:13:51

This pack contains workflow and data for hybrid recommender that is created by combining Recommender extension and RapidMiner built in operators.

2 items in this pack

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RapidMiner

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Ninoaf

Online update recommendation web service (1)

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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

RapidMiner

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Ninoaf

Write activity web service (1)

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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

RapidMiner

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Matija Piskorec

Execution time measurement for Rapidminer ... (1)

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This is a workflow that measures execution times of various RapidMiner operators on random polynomial classification datasets of various sizes in the range of 4 to 2000 examples and attributes.

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

RapidMiner

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Ninoaf

Front-end Recommendation Web Service (1)

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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

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Ninoaf

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Recommender system engine in RapidAnalytics

Created: 2012-05-11 12:54:44 | Last updated: 2012-06-20 14:47:43

Here we provide four workflows that build the recommender engine by using the recommender extension. The Recommender Extension can be downloaded from the Rapid-I Marketplace from: http://rapidupdate.de:8180/UpdateServer/faces/product_details.xhtml?productId=rmx_irbrecommender . More details can be found: http://elico.rapid-i.com/recommender-extension.html

9 items in this pack

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Mohd Saberi

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Prediction of Protein Residue Contact Using Support ...

Created: 2012-05-11 02:44:49

Credits: Mohd Saberi

License: Creative Commons Attribution-Share Alike 3.0 Unported License

Prediction of protein residue contact is one of the important two-dimensional prediction tasks in protein structure prediction. The residue contact map of protein contains information which represents three-dimensional conformation of protein. However the accuracy of the prediction is dependent on the type of protein information used to distinguish between contacts or noncontacts. According to CASP (Critical Assessment of Techniques of Protein Structure Prediction) the accuracy o...

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Mohd Saberi

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Predicting Protein Secondary Structure with Neural N...

Created: 2012-05-11 02:34:20

Credits: Mohd Saberi

License: Creative Commons Attribution-Share Alike 3.0 Unported License

Protein secondary structure prediction has benefited largely from the improvements in machine learning techniques. A novel neural network classifier based on the Dempster-Shafer theory has been introduced by Denoeux. It demonstrated favourable performance in some artificial and real world problems when compared against methods such as Support Vector Machines and Kernel Fisher Discriminants. However, it has not been used for protein secondary structure prediction. In this work, we examin...

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Mohd Saberi

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Pathway-Based Microarray Analysis for Defining Stati...

Created: 2012-05-11 02:33:00

Credits: Mohd Saberi

License: Creative Commons Attribution-Share Alike 3.0 Unported License

In this reiew, we have discussed about approaches in pathway based microarray analysis. Commonly, there are two approaches in pathway based analysis, Enrichment Score and Supervised Machine Learning. These pathway based approaches usually aim to statistically define significant pathways that related to phenotypes of interest. Firstly we discussed an overview of pathway based microarray analysis and its general flow processes in scoring the pathways, the methods applied in both a...

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