B2B Customer Conversion Prediction: A Document Representation, Graph Theory, and CatBoost Driven Methodology

arXiv:2609.03239 · cs.LG · Submitted 2026-09-03 · Read on arXiv

cs.LG

Submitted: 2026-09-03

Updated: 2026-09-11

Comments: 10 pages, 6 figures. Presented at the 2nd Workshop on End-End Customer Journey Optimization at KDD 2023

Code: https://github.com/seatgeek/fuzzywuzzy

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Clustering — 26× in this paper · explained in 2 episodes
• Feature Selection — 14× in this paper · explained in 4 episodes
• Accuracy — 10× in this paper · explained in 1 episode
• SHAP values — 10× in this paper · explained in 2 episodes
• Aggregation — 5× in this paper · explained in 1 episode
• Feature Extraction — 5× in this paper · explained in 2 episodes
• AUC — 2× in this paper · explained in 2 episodes
• Vector Representations — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Area Under the Curve (AUC) — 1× in this paper · explained in 1 episode
• Causal Inference — 1× in this paper · explained in 4 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• International Joint Conference on Neural Networks — 1× in this paper
• Perplexity — 1× in this paper · explained in 3 episodes
• Predictive Modeling — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks