Multiview Machine Learning Method

This is the machine learning method implemented for the article:

Dalip, Daniel Hasan, Gonçalves, Marcos A., Cristo, Marco, & Calado, Pavel (2017). A general multiview framework for assessing the quality of collaboratively created content on web 2.0. Journal of the Association for Information Science and Technology, 68(2), 286-308.

This software is compatible with Linux only.

Usage

First, prepare a training and testing dataset in libsvm the format (examples in folder toyExample):

<class> <id_feature_1>:<val_feature_1> <id_feature_2>:<val_feature_2> ... <id_feature_n>:<val_feature_n>

where <class> is the target class <id_feature_i> is the ith feature id (starting with 1) and <val_feature_i> is the ith feature value. After that, configure the multiview method setting the config variables (you can change the default parameters in the configExample.cnf or configExample_l2r.cnf). Also in this file you will assign the view for each feature. For more information, see .cnf file comments. To run this program, use:

java -jar multiview.jar <train-file> <test-file> <config-file>

Example:

java -jar multiview.jar toyExample/train_svm.txt toyExample/test_svm.txt configExample.cnf

The source code is available at the MultiviewMethod folder. Note that you can use different methods by changing the XML MultiviewMethod/learning_methods.xml and creating their scripts. Regarding Learning to Rank method, we used the SVM-RANK library available at: https://www.cs.cornell.edu/peopl. Use the same format as SVM-RANK in case of L2R problems.

In case of having problems with the file multiview.jar, you may want to recompile it:

ant -buildfile multiview_ant.xml

Quality Assessment Method Datasets and Results

The dataset and results for assessing the quality of content regarding the Question and Answering Forums and Wikis datasets are avaliable for download in their respective links.