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.
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
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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