Feedback Forensics: An open-source toolkit to measure AI personality
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Updated
May 7, 2026 - Python
Feedback Forensics: An open-source toolkit to measure AI personality
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.
A personality-aware group recommendation system based on pairwise preferences
Adversarial Preference Learning with Pairwise Comparisons for Group recommendation System
Two Group Recommendation Approaches based on the Contribution of the Users and Pairwise Preferences
Group Recommendation Systems with Diversity-based Clustering and Game Theory
Consistency-aware ranking from pairwise preferences: cyclic preference-graph repair (MWFAS), retrieval evaluation, and statistical-inference methodology. Companion code for a manuscript submitted to SN Computer Science.
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