mirror of
https://github.com/hanzoai/papers.git
synced 2026-10-29 07:34:31 +00:00
zeekay
0d1d121451
Fix shared/ include paths to ../shared/ for 55 subdirectory papers so
that hanzocover.sty and lstlang.tex resolve when building from each
paper's own directory (the build pattern used by latexmk per paper).
Build fixes:
- hanzo-operative/sections/03-screen-understanding-pipeline.tex: move
\COMMENT from after \FOR{...} onto the \STATE line so the algorithmic
env doesn't mis-parse.
- hanzo-network-architecture (abstract + sections 04, 12, 13): escape
unicode mu (U+03BC) as $\mu$ for pdflatex.
- ntt-transform/sections/08-benchmark-results.tex: same mu escape.
All 55 Tier-2/3 papers now build clean with latexmk -pdf.
71 lines
2.3 KiB
TeX
71 lines
2.3 KiB
TeX
\documentclass[11pt,a4paper]{article}
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\usepackage{../shared/hanzocover}
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% Packages
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\usepackage[utf8]{inputenc}
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\usepackage[T1]{fontenc}
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\usepackage{amsmath,amssymb,amsthm}
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\usepackage{graphicx}
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\usepackage{hyperref}
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\usepackage{algorithm}
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\usepackage{algpseudocode}
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\usepackage{booktabs}
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\usepackage{listings}
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\input{../shared/lstlang}
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\usepackage{xcolor}
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\usepackage[numbers]{natbib}
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\usepackage{tikz}
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\usetikzlibrary{arrows,shapes,positioning,calc}
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% Theorem environments
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\newtheorem{theorem}{Theorem}[section]
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\newtheorem{lemma}[theorem]{Lemma}
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\newtheorem{proposition}[theorem]{Proposition}
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\newtheorem{definition}[theorem]{Definition}
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\newtheorem{corollary}[theorem]{Corollary}
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% Code listing style
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\lstset{
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basicstyle=\ttfamily\small,
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keywordstyle=\color{blue},
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commentstyle=\color{gray},
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stringstyle=\color{red},
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breaklines=true,
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frame=single,
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numbers=left,
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numberstyle=\tiny\color{gray}
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}
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\title{\textbf{Machine Learning for Commerce Analytics:\\Predictive Models for Customer Behavior}}
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\author{
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Zach Kelling\\
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Hanzo Industries\\
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\texttt{[email protected]}
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}
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\date{October 2018}
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\begin{document}
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\hanzocoverpage
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\begin{abstract}
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We present Hanzo Analytics, a machine learning platform for e-commerce that provides predictive analytics, customer segmentation, and churn prediction. Our system processes behavioral event streams in real-time to construct customer profiles and predict future actions. We introduce a novel feature engineering pipeline that extracts 847 features from raw event data, enabling predictive models that achieve 0.89 AUC for purchase prediction and 0.84 AUC for churn prediction. Customer segmentation using our modified k-means algorithm with behavioral embeddings improves marketing campaign ROI by 2.3x compared to demographic-only segmentation. The platform processes 50M events daily across 500+ merchants.
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\end{abstract}
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\input{sections/01-introduction}
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\input{sections/02-system-architecture}
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\input{sections/03-feature-engineering}
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\input{sections/04-predictive-models}
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\input{sections/05-customer-segmentation}
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\input{sections/06-real-time-scoring}
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\input{sections/07-model-training-pipeline}
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\input{sections/08-evaluation}
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\input{sections/09-privacy-and-compliance}
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\input{sections/10-related-work}
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\input{sections/99-conclusion}
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\end{document}
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