Adversarial Preference Learning with Pairwise Comparisons for Group recommendation System
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Updated
Feb 29, 2024 - Jupyter Notebook
Adversarial Preference Learning with Pairwise Comparisons for Group recommendation System
Compact Letter Display (CLD) in Julia
A Jupyter notebook for a project centered around 'Group Recommendation Systems (GRS)' utilizing the 'GcPp' clustering approach.
ez-rate provides an easy method to a pairwise A/B comparison of images
Analysis of Swiss-system tournaments
For this Project, I first applied an analysis of variance (ANOVA) model to the Pymaceutical dataset and then did a post-hoc analysis of the results by using Tukey Honest Significant Difference (HSD) to determine which drug treatments in the dataset significantly reduce tumor volume and metastasis. I then wrote a summary of my findings.
[CRBHits](https://github.com/kullrich/CRBHits) is a reimplementation of the Conditional Reciprocal Best Hit algorithm [crb-blast](https://github.com/cboursnell/crb-blast) in R.
The salary dataset contains info on 474 Midwestern bank employees. Tasks include understanding the dataset's structure, summarizing numerical variables, testing hypotheses on salary equality, gender-based differences, age group analysis, and proportion comparison.
Fast, large scale library for computing rankings and features based on various pairwise and graph algorithms
A web application to rate texts using pairwise comparison.
Pairwise sample similarity (cosine) between records.
Framework for using LLMs to grade texts by using pairwise comparisons.
Analysis of real estate sales data. Tasks include understanding dataset structure, variable conversion, descriptive analysis, pairwise comparisons, linear relationship analysis, multiple regression modeling, feature selection using stepwise methods, final model summary, assumptions checking, and LASSO variable selection. Results are documented.
This web application allows users to compare multiple items in a pairwise fashion and helps determine which item is preferred. The application dynamically handles comparisons across different user-created categories.
Code for "Learning the Relation Between Mobile Encounters and Web Traffic Patterns: A Data-driven Study" MSWiM 2018 (ACM: https://dl.acm.org/citation.cfm?id=3242137, Tech Report: https://arxiv.org/abs/1808.03842)
An easy to use Amazon Recommendation System using Cosine Similarity.
Very detailed exploratory data analysis is executed on the dataset. Univariate and bivariate analysis using ANOVA and Chi-Squared Test between continuous and categorical variables are explored to find out the relationship between input variables and the output target 'revenue'.
Derives a ranking from pairwise comparisons
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