NJU LAMDA Thesis Template
Autor
Zhuang Zhenhua
Letzte Aktualisierung
vor 2 Jahren
Lizenz
Creative Commons CC BY 4.0
Abstrakt
NJU LAMDA Thesis Template
NJU LAMDA Thesis Template
\documentclass[aspectratio=169]{beamer}%页面比例16:9
\setbeamercovered{dynamic}%半透明显示
\setbeamertemplate{navigation symbols}{}
\usepackage[backend=bibtex,sorting=none,style=numeric]{biblatex}%设置引用
\addbibresource{ref.bib} %bib数据文件位置
\usepackage{dashrule}
\definecolor{NJUPurple}{rgb}{0.28235, 0.28235, 0.62745}%设置主题颜色
\colorlet{LightNJUPurple}{white!60!NJUPurple}
\colorlet{SuperLightNJUPurple}{white!90!NJUPurple}
\usecolortheme[named=NJUPurple]{structure}
\usepackage[utf8]{inputenc}
\usepackage{graphicx} % Allows including images
\usepackage{booktabs} % Allows the use of \toprule, \midrule and \bottomrule in tables
\usepackage{subfigure}
\usepackage{subfiles}
\usepackage{url}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{xcolor,colortbl}
\usepackage[AutoFakeBold, AutoFakeSlant]{xeCJK}
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\renewcommand{\today}{\number\year .\number\month .\number\day }
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\def\insertfootnotetext{ #1}%
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\vskip-0.2cm%<- changed
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\setbeamertemplate{section in toc}[circle] % 目录前设置序号
\setbeamerfont{frametitle}{series=\bfseries}% 标题加粗
\setbeamerfont{framesubtitle}{size=\fontsize{11}{11},series=\bfseries}
\usepackage{latexsym,amsmath,xcolor,multicol,booktabs,calligra}
\usepackage{graphicx,pstricks,listings,stackengine}
\usepackage{wasysym}
%Contents before every section's starting slide
\AtBeginSection[]
{
\begin{frame}
\frametitle{Outline}
\tableofcontents[
currentsection,
currentsubsection,
subsectionstyle=show/show/hide,
sectionstyle=show/shaded
]
\end{frame}
}
% 首页修改
\title{Your Title Your Title Your Title Your Title Your Title Your Title Your Title }
\subtitle{Conference 2024}
\institute{Your Institution}
\author{汇报人:君の名は}
\date{\today}
% 脚注修改
\defbeamertemplate{footline}{NGEGFootlineTemplate}{%
\leavevmode% 离开vmode,也就是离开竖直模式,进入水平模式
\begin{beamercolorbox}[wd=0.975\paperwidth,ht=2.25ex,dp=3ex,right]{title in head/foot}%
\ifnum \the\value{page}>1 \text{\href{http://www.lamda.nju.edu.cn}{http://www.lamda.nju.edu.cn}}\fi
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% \vskip0pt%
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\setbeamertemplate{footline}[NGEGFootlineTemplate]
%+++++++++++++++++++++++++++++++++++正文开始+++++++++++++++++++++++++++++++++++%
\begin{document}
{
\usebackgroundtemplate{\includegraphics[width=\paperwidth,height=\paperheight]{figs/cover.pdf}}
\frame{\titlepage}
}
{
\usebackgroundtemplate{\includegraphics[width=\paperwidth,height=\paperheight]{figs/content.pdf}}
\begin{frame}
\frametitle{About the Author(分栏显示)}
\begin{columns}
\begin{column}{0.37\linewidth}
\begin{figure}
{\includegraphics[scale=0.16]{figs/profile.jpg}}
\end{figure}
{\scriptsize
{\textbf{LinaBell}}\\Research Fellow in Nanjing University, a member of LAMDA.
}
\end{column}
\begin{column}{0.65\linewidth}
\textbf{Latest works}
\begin{itemize}
\item NeurPIS
\item ICML
\item ICRL
\item TPAMI
\item PRML
\end{itemize}
\end{column}
\end{columns}
\end{frame}
% 储备知识部分
\section{Preliminary}
\begin{frame}
\frametitle{Preliminary(公式展示)}
\begin{itemize}
\item{\textbf{Learning Strategy}}
\\ \hspace*{\fill} \\
Optimization methods:
Pointwise loss (binary cross-entropy, mean square error), pairwise loss (BPR, WARP), and {\color{red}softmax loss}
\begin{gather}
\mathcal{L}_0 = -\sum_{(u,i)\in{O}^{+}}\log\frac{\exp{(\cos(\hat{\theta}_{ui})/\tau)}}{\exp{(\cos(\hat{\theta}_{ui})/\tau)}+\sum_{j\in{N}_{u}}\exp{(\cos(\hat{\theta}_{uj})/\tau)}},
\nonumber
\end{gather}
\end{itemize}
\end{frame}
% 相关工作
\section{Related Work}
\begin{frame}
\frametitle{Related Work(多级列表)}
\textbf{SOTA debiasing strategies}
\begin{itemize}
\item
\textbf{Sample re-weighting methods} (e.g. IPS-CN)\\
exploit the item popularity's inverse to re-weight loss of each instance.
\item
\textbf{Causal inference methods} (e.g. MACR, CausE)\\
\begin{itemize}
\item
specify the role of popularity bias in assumed causal
graphs
\item
mitigate the bias effect on the prediction.
\end{itemize}
\item
{
\textbf{Regularization-based frameworks} (e.g. Sam-reg) \\
\begin{itemize}
\item Provides a tunable mechanism for controlling the trade-off between recommendation accuracy and coverage.\\
\item
\textbf{Sam-reg} regularizes the biased correlation between user-item relevance and item popularity
\end{itemize}}
\end{itemize}
\end{frame}
% 方法部分
\section{Methodology}
\subsection{BC Loss}
\begin{frame}
\frametitle{Methodology of BC Loss}
\framesubtitle{BC Loss(二级标题)}
\begin{itemize}
\item
\textbf{BC Loss}
\begin{align}\label{equ:bc_loss}
\mathcal{L}_{\text{BC}} =
-\sum_{(u,i)\in{O}^{+}}\log\frac{\exp{(\cos(\hat{\theta}_{ui}{\color{red}+M_{ui}})/\tau)}}{\exp{(\cos(\hat{\theta}_{ui}{\color{red}+M_{ui}})/\tau)}+\sum_{j\in{N}_{u}}\exp{(\cos(\hat{\theta}_{uj})/\tau)}},
\nonumber
\end{align}
$M_{ui}$: the bias-aware angular margin for the interaction $(u,i)$
$$M_{ui} = \min \{\hat{\xi}_{ui}, \pi - \hat{\theta}_{ui}\}$$
\item\textbf{Intuition}\\
If a user-item pair is the hard interaction that can hardly be reconstructed by its popularity statistics, it holds a
high value of $\xi_{ui}$ and leads to a high value of $M_{ui}$. Henceforward, BC loss imposes the large angular
margin $M_{ui}$ between the negative item $j$ and positive item $i$.
\end{itemize}
\end{frame}
% 分析部分
\section{Analyses}
\subsection{Geometric Interpretation}
\begin{frame}
\frametitle{Analyses(图像展示)}
\framesubtitle{Geometric Interpretation}
\begin{itemize}
\item
\textbf{Geometric Interpretation}\\
User $u$ with one observed item $i$ and two unobserved items $j$ and $k$.\\
\begin{figure}
{\includegraphics[scale=0.92]{figs/fig.pdf}}
\end{figure}
\end{itemize}
\end{frame}
\subsection{Theoretical Properties}
\begin{frame}
\frametitle{Analyses(数学环境)}
\framesubtitle{Theoretical Properties}
\begin{itemize}
\item
\textbf{Theoretical Properties}
\begin{proof}
1. There exists an upper bound $m$, s.t. $-1 < \cos(\hat{\theta}_{ui}+M_{ui}) \leq {v}_u^T{v}_i - m < 1 $\\
2. \\
3. \\
4. \\
5. \\
6. \\
\end{proof}
\end{itemize}
\end{frame}
% 实验部分
\section{Experiments}
\begin{frame}
\frametitle{Experiments(表格展示)}
\framesubtitle{Baselines \& Datasets}
\textbf{Baselines}
\begin{itemize}
\item
Backbone: only use softmax loss
\item
IPS-CN: sample re-weighting methods
\item
CausE: bias removal by causal inference
\item
sam + reg: regularization-based framework
\item
MACR: bias removal by causal inference
\end{itemize}
\textbf{Datasets}
\resizebox{\columnwidth}{!}{
\begin{tabular}{lrrrrrrr}
\toprule
& KuaiRec & Douban Movie & Tencent & Amazon-Book & Alibaba-iFashion & Yahoo!R3 & Coat\\ \midrule
\#Users & 7175 & 36,644 & 95,709 & 52,643 & 300,000 & 14382 & 290 \\
\#Items & 10611 & 22,226 & 41,602 & 91,599 & 81,614 & 1000 & 295 \\
\#Interactions & 1062969 & 5,397,926 & 2,937,228 & 2,984,108 & 1,607,813 & 129,748 & 2,776 \\
Sparsity & 0.01396 & 0.00663 & 0.00074 & 0.00062 & 0.00007
& 0.00902 & 0.03245\\ \bottomrule
\end{tabular}}
\end{frame}
% 结论部分
\section{Conclusion}
\begin{frame}
\frametitle{Conclusion(脚注使用)}
\begin{itemize}
\item
\textbf{Contribution}\\
\begin{itemize}
\item
(Originality) Popular bias extractor has an intuitive geometric interpretation.
\item
(Quality) Outperforms existing methods in various evaluation protocols.
\item
(Clarity) Well-written and easy to understand. Theoretical proof is quite solid.
\end{itemize}
\item
\textbf{Limitation}\\
\begin{itemize}
\item
The technical contribution of this paper is limited.It only proposes to employ an extra popularity-based predictor and combine the results with an existing CF model\footfullcite{he2020momentum}.
\item
Overclaims the strength of the proposed BC loss in theoretical analysis. The geometric interpretability and the hard-negative mining ability are actually the same thing\parencite{ pmlr-v119-wang20k, yuan2021one}
\end{itemize}
\end{itemize}
\end{frame}
% 引用部分
\section*{References}
\begin{frame}[allowframebreaks]
\frametitle{References}\color{NJUPurple}{
\printbibliography[heading=none]}
\end{frame}
% 谢辞部分
\section*{Acknowledgement}
\begin{frame}
\frametitle{Acknowledgement}
\textcolor{NJUPurple}{\Huge{\centerline{Thank you!}}}
\end{frame}
}
\end{document}