% by Mirella M. Moro; version: January/18/2012 @ 04:16pm
% -- 01/18/2012: more discussion on SBBD + JIDM; overall revision
% -- 09/03/2010: bib file with names for proceedings and journals; cls with shrinked {received}
% -- 08/27/2010: appendix, table example, more explanation within comments, editors' data

\documentclass[jidm,a4paper]{jidm} % NOTE: JIDM is published on A4 paper
\usepackage{graphicx,url}  % for using figures and url format
\usepackage[T1]{fontenc}   % avoids warnings such as "LaTeX Font Warning: Font shape 'OMS/cmtt/m/n' undefined"

\usepackage{algorithm}
\usepackage{algpseudocode}
\usepackage{pifont}
\usepackage{subfigure}			% Inclusão de subfiguras
\usepackage{epstopdf}
\usepackage{rotating}
\usepackage{multirow}
\usepackage{tablefootnote}
\usepackage[table]{xcolor}
\usepackage{enumitem}
\usepackage{amssymb}
\usepackage[utf8]{inputenc}

%\usepackage{cite} % NOTE: do **not** include this package because it conflicts with jidm.bst

% Standard definitions
\newtheorem{theorem}{Theorem}[section]
\newtheorem{conjecture}[theorem]{Conjecture}
\newtheorem{corollary}[theorem]{Corollary}
\newtheorem{proposition}[theorem]{Proposition}
\newtheorem{lemma}[theorem]{Lemma}
\newdef{definition}[theorem]{Definition}
\newdef{remark}[theorem]{Remark}

% New environment definition
\newenvironment{latexcode}
{\ttfamily\vspace{0.1in}\setlength{\parindent}{18pt}}
{\vspace{0.1in}}

% ALL FIELDS UNTIL BEGIN{document} ARE MANDATORY

%% The following data (volume, number and page) are given by the editors prior to publishing your article
%\jidmVolume{5}
%\jidmNumber{1}
%\jidmYear{17}
%\jidmMonth{June}
%\setcounter{page}{1}

% Includes headers with simplified name of the authors and article title
\markboth{N.Silva et al}
{Non-personalized Movie Recommendation by Maximum Coverage}
% Diversify to Conquer: Using Maximum Coverage in Recommendation Systems
%  -> \markboth{}{}
%         takes 2 arguments
%         ex: \markboth{M. M. Moro}{Any article title}


% Title of the article
\title{\textbf{Non-personalized Movie Recommendation by\\Maximum \textit{k}-Coverage}}


% List of authors
%IF THERE ARE TWO or more institutions, please use:
%\author{Name of Author1\inst{1}, Name of Author2\inst{2}, Name of Author3\inst{2}}
\author{Nícollas Silva\inst{1}, Adriano César\inst{1}, Leonardo Rocha\inst{2}, Diego Carvalho\inst{2}, Fernando Mourão\inst{3}}


%Affiliation and email
\institute{Universidade Federal de Minas Gerais, Brazil \\ \email{ncsilvaa@gmail.com} \email{adrianoc@ufmg.br}
\and Universidade Federal de São João del-Rei, Brazil \\ \email{lcrocha@ufsj.edu.br} \email{diegocarvalho426@gmail.com}
\and Seek Al Labs \\ \email{fhmourao@seek.com}
% IF THERE IS ANOTHER INSTITUTION:
%\and Name_of_the_second_institution \\
%\email{address@whatever.com}
}


% Article abstract - it should be from 100 to 300 words
\begin{abstract}
Turning first-time users into returning ones is a major task for the success of e-commerce systems. However, providing effective recommendations for these users remains as an open challenge for the area due to the absence of consumption information. In this context, non-personalized RSs emerge as the main approach adopted in real scenarios. Such approaches are based on the premise that the consumption is generally biased towards items that arouse interest in the majority of a population. Despite being valid for mass consumption, by adopting this premise RSs fail to help users interested in items different from the common taste. In this context, this work proposes a new RSs based on Maximum $k$-Coverage strategy to merge popular and non-popular items in order to retain different profiles of first-time users. The premise of this approach is that maximizing diversity, while maintaining the relevance of the recommended items, satisfies the preferences of different user profiles. Indeed, the results show a mean gain of 13.5\% w.r.t. utility and diversity, when compared to traditional strategies based on Popularity, Best Rated and Recent Items. In addition, our results indicate that the proposed strategy is able to smooth the popularity bias in recommendations, satisfying at least 97\% of different users.
\end{abstract}


% ACM Computing Classification System categories
\category{H.3.3}{Retrieval tasks and goals}{Recommender Systems} 

% Categories and Descriptors are available at the 1998 ACM Computing Classification System
% http://www.acm.org/about/class/1998/
%  -> \category{}{}{}
%         takes 3 arguments for the Computing Reviews Classification Scheme.
%         ex: \category{D.3.3}{Programming Languages}{Language Constructs and Features}
%                   [data types and structures]
%                   the last argument, in square brackets, is optional.

% Article keywords
\keywords{Recommender Systems, Maximum Coverage, Ramp-up Problem, Diversify}
%  -> \keywords{} (in alphabetical order \keywords{document processing, sequences,
%                      string searching, subsequences, substrings})


% THE ARTICLE BEGINS
\begin{document}

% This is optional:
\begin{bottomstuff}
% similar to \thanks
% for authors' addresses; research/grant statements
\end{bottomstuff}

\maketitle

\input{sec01_introducao}

\input{sec02_trabRelacionados}

\input{sec03_maximum_coverage}

\input{sec04_ProjExperimental}

\input{sec05_resultados}

\input{sec06_conclusao}

\bibliographystyle{ieeetr}
\bibliography{jdim2017}

\end{document}
