Performs pairwise preference ranking for a given trainfile and testfile with binary class labels (1 and not 1). The binary classification on the pairwise test data gives a prediction from each pair of test items: which of the two should be ranked higher. From these pairwise preferences a ranking can be created using a greedy sort algorithm.

Predicting missing pairwise preferences from similarity features in group decision making and group recommendation system

A Jupyter notebook for a project centered around 'Group Recommendation Systems (GRS)' utilizing the 'GcPp' clustering approach.

Adversarial Preference Learning with Pairwise Comparisons for Group recommendation System

A personality-aware group recommendation system based on pairwise preferences

Two Group Recommendation Approaches based on the Contribution of the Users and Pairwise Preferences

Group Recommendation Systems with Diversity-based Clustering and Game Theory