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                        <h1>WORKSHOP INFORMATION</h1>
                        <div style="width:auto;height:3px; background:#262626;"></div>
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                <div class="col-md-12 center" style="float: left; margin-top:40px">
                    <div class="col-md-12 workshop-unit" id="eyre19">
                        <div class="workshop-name ">
                            <h3>EYRE 2019</h3>
                            <p>The 2nd International Workshop on EntitY REtrieval</p>
                            <p style="font-size: 16px; margin-top: 20px; padding-left: 20px;;">
                                Room 303A, 9:30AM-12:30PM Nov 7
                            </p>
                        </div>
                        <div class="workshop-info ">
                            <ul class="fa-ul">
                                <li><i class="fa-li fa fa-users"></i>
                                    <dt>Organizer</dt>
                                    <dd>
                                        <ul>Gong Cheng (Nanjing University, China)</ul>
                                        <ul>Kalpa Gunaratna (Samsung Research America, USA)</ul>
                                        <ul>Jun Wang (University College London, UK)</ul>
                                    </dd>
                                </li>
                                <li class="workshop-desc"><i class="fa-li fa fa-bookmark"></i>
                                    <dt>Description</dt>
                                    <dd>
                                        <p>The 2nd EYRE workshop welcomes contributions related to any aspect of entity
                                            retrieval and semantic search. We also organize two shared tasks: entity
                                            summarization and entity search.</p>
                                    </dd>
                                    <div class="more"><a><span style="margin-right: 5px;">More</span><i
                                                class="fa fa-chevron-down" aria-hidden="true"></i></a></div>
                                </li>
                                <li><i class="fa-li fa fa-desktop"></i>
                                    <dt><a href="https://sites.google.com/view/eyre19" target="_blank">URL
                                            <i class="fa fa-angle-right" aria-hidden="true"></i></a></dt>
                                </li>
                            </ul>
                        </div>
                    </div>
                    <div class="col-md-12 workshop-unit" id="bigscholar2019">
                        <div class="workshop-name ">
                            <h3>BigScholar 2019</h3>
                            <p>The 6th Workshop on Big Scholarly Data</p>
                            <p style="font-size: 16px; margin-top: 20px; padding-left: 20px;;">
                                Room 302A, 9:30AM-12:30PM Nov 7
                            </p>
                        </div>
                        <div class="workshop-info ">
                            <ul class="fa-ul">
                                <li><i class="fa-li fa fa-users"></i>
                                    <dt>Organizer</dt>
                                    <dd>
                                        <ul>Feng Xia (Dalian University of Technology, China)</ul>
                                        <ul>Huan Liu (Arizona State University, USA)</ul>
                                        <ul>Irwin King (The Chinese University of Hong Kong, China)</ul>
                                        <ul>Kuansan Wang (Microsoft Research, USA)</ul>
                                    </dd>
                                </li>
                                <li class="workshop-desc"><i class="fa-li fa fa-bookmark"></i>
                                    <dt>Description</dt>
                                    <dd>
                                        <p>The BigScholar 2019 workshop aims at bringing together academics and
                                            practitioners from diverse fields to share ideas and experience with
                                            management, analysis, mining, and applications of big scholarly data. The
                                            goal is to contribute to the birth of a community having a shared interest
                                            around big scholarly data and exploring it using knowledge discovery, data
                                            science and analytics, network science, and other appropriate technologies.
                                        </p>
                                    </dd>
                                    <div class="more"><a><span style="margin-right: 5px;">More</span><i
                                                class="fa fa-chevron-down" aria-hidden="true"></i></a></div>
                                </li>
                                <li><i class="fa-li fa fa-desktop"></i>
                                    <dt><a href="http://thealphalab.org/bigscholar/" target="_blank">URL
                                            <i class="fa fa-angle-right" aria-hidden="true"></i></a></dt>
                                </li>
                            </ul>
                        </div>
                    </div>
                    <div class="col-md-12 workshop-unit" id="kdahcikm2019">
                        <div class="workshop-name ">
                            <h3>KDAH-CIKM 2019</h3>
                            <p>The 2nd Workshop on Knowledge-driven Analytics and Systems Impacting Human Quality of
                                Life</p>
                                <p style="font-size: 16px; margin-top: 20px; padding-left: 20px;;">
                                    Room 302B, 9:30AM-12:30PM Nov 7
                                </p>
                            </div>
                        <div class="workshop-info ">
                            <ul class="fa-ul">
                                <li><i class="fa-li fa fa-users"></i>
                                    <dt>Organizer</dt>
                                    <dd>
                                        <ul>Arijit Ukil (Research and Innovation, Tata Consultancy Services, India)</ul>
                                        <ul>Leandro Marin (University of Murcia, Spain)</ul>
                                        <ul>Antonio Jara (University of Applied Sciences Western Switzerland (HES-SO),
                                            Switzerland)</ul>
                                        <ul>John Farserotu (Centre Suisse d'Electronique et de Microtechnique (CSEM),
                                            Switzerland)</ul>
                                    </dd>
                                </li>
                                <li class="workshop-desc"><i class="fa-li fa fa-bookmark"></i>
                                    <dt>Description</dt>
                                    <dd>
                                        <p>The main focus of this workshop is to bring proposals and insights that
                                            demonstrate the knowledge-driven technologies, developments and applications
                                            for ensuring improvement of human quality of life. The intended thrust is to
                                            promote the development of human-centric intelligent technologies like
                                            precise and personalized medication and prognosis prediction, improved
                                            elderly care, minimizing private data theft, knowledge-driven energy and
                                            resource management, deep learning and artificial intelligence based
                                            applications for risk prediction and augmented human capability generation.
                                            We expect researchers in the field of knowledge management, information
                                            retrieval, artificial intelligence, data mining, privacy analytics will
                                            provide insights of technological aspects as well as application-specific
                                            scenarios of human-centric knowledge-driven analytics and systems. The areas
                                            of interest, not limited to: Clinical Analytics; Privacy Preserving Data
                                            Mining; Recommender systems for retail, financial decision making; Fraud
                                            detection and prevention system; Human cognition analysis; Knowledge-driven
                                            human action understanding and decision making; Deep learning and artificial
                                            intelligence based applications; Social network analysis and related others.
                                        </p>
                                    </dd>
                                    <div class="more"><a><span style="margin-right: 5px;">More</span><i
                                                class="fa fa-chevron-down" aria-hidden="true"></i></a></div>
                                </li>
                                <li><i class="fa-li fa fa-desktop"></i>
                                    <dt><a href="https://sites.google.com/view/kdah-cikm-2019/home" target="_blank">URL
                                            <i class="fa fa-angle-right" aria-hidden="true"></i></a></dt>
                                </li>
                            </ul>
                        </div>
                    </div>
                    <div class="col-md-12 workshop-unit" id="kars2019">
                        <div class="workshop-name ">
                            <h3>KARS 2019</h3>
                            <p>Knowledge-aware and Conversational Recommender Systems Workshop</p>
                            <p style="font-size: 16px; margin-top: 20px; padding-left: 20px;;">
                                Room 303B, 9:30AM-12:30PM Nov 7
                            </p>
                        </div>
                        <div class="workshop-info ">
                            <ul class="fa-ul">
                                <li><i class="fa-li fa fa-users"></i>
                                    <dt>Organizer</dt>
                                    <dd>
                                        <ul>Vito Walter Anelli (Polytechnic University of Bari, Italy)</ul>
                                        <ul>Tommaso Di Noia (University of Murcia, Spain)</ul>
                                        <ul>Antonio Jara (Polytechnic University of Bari, Italy)</ul>
                                    </dd>
                                </li>
                                <li class="workshop-desc"><i class="fa-li fa fa-bookmark"></i>
                                    <dt>Description</dt>
                                    <dd>
                                        <p>The workshop focuses on all the aspects related to the injection and adoption
                                            of knowledge sources in Recommender Systems. More and more precise and
                                            powerful recommendation algorithms and techniques have been proposed over
                                            the last years able to effectively assess users' tastes and predict
                                            information that would probably be of interest for them. Most of these
                                            approaches rely on the collaborative paradigm (often exploiting machine
                                            learning techniques) and do not take into account the huge amount of
                                            knowledge, both structured and non-structured ones, describing the domain of
                                            interest for the recommendation engine. The aim of knowledge-aware and
                                            conversational recommender systems is to go beyond the traditional accuracy
                                            goal and to start a new generation of algorithms and interactive approaches
                                            which exploit the knowledge encoded in ontological and logic-based knowledge
                                            bases, knowledge graphs as well as the semantics emerging from the analysis
                                            and exploitation of semi-structured textual sources.</p>
                                    </dd>
                                    <div class="more"><a><span style="margin-right: 5px;">More</span><i
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                                </li>
                                <li><i class="fa-li fa fa-desktop"></i>
                                    <dt><a href="http://sisinflab.poliba.it/kars/2019/" target="_blank">URL
                                            <i class="fa fa-angle-right" aria-hidden="true"></i></a></dt>
                                </li>
                            </ul>
                        </div>
                    </div>
                    <div class="col-md-12 workshop-unit" id="dtmbio2019">
                        <div class="workshop-name ">
                            <h3>DTMBIO 2019</h3>
                            <p>The 13th International Workshop on Data and Text Mining in Biomedical Informatics</p>
                            <p style="font-size: 16px; margin-top: 20px; padding-left: 20px;;">
                                Room 305, 9:30AM-5:00PM Nov 7
                            </p>
                        </div>
                        <div class="workshop-info ">
                            <ul class="fa-ul">
                                <li><i class="fa-li fa fa-users"></i>
                                    <dt>Organizer</dt>
                                    <dd>
                                        <ul>Hyojung Paik (Korea Institute of Science and Technology)</ul>
                                        <ul>Jian Tang (HEC Montreal and Montreal Institute for Learning Algorithms,
                                            Canada)</ul>
                                        <ul>Ruibion Xi (Peking University)</ul>
                                        <ul>Sangwoo Kim (Yonsei University College of Medicine)</ul>
                                        <ul>Dokyun Na (Chung-Ang University)</ul>
                                    </dd>
                                </li>
                                <li class="workshop-desc"><i class="fa-li fa fa-bookmark"></i>
                                    <dt>Description</dt>
                                    <dd>
                                        <p>The rapid growth of medical and health informatics field is tightly coupled
                                            with developments within several areas in computer science, including data
                                            and text mining, information extraction and database management systems,
                                            among others. A fundamental topic of research within medical informatics is
                                            how to make effective use of the tremendous amount of biological and
                                            biomedical data to improve the understanding of biological systems. Such
                                            data include, but are ot limited to, gene and protein sequences, gene
                                            expression profiles, protein structure predictions resulting from
                                            high-throughput computational methods, protein-protein interactions from
                                            proteomic studies, Single Nucleotide Polymorphisms (SNPs) profiles, and
                                            information from the literature and other textual resources. The need to
                                            extract, understand, integrate, and make use of nformation embedded in such
                                            heterogeneous unstructured data, automatically and effectively drives the
                                            current research in medical and healthcare informatics. </p>
                                        <p>The focus of this workshop is to bring together researchers that work in the
                                            areas of data and text mining and computational biology, interested in
                                            integrating such heterogeneous structured and unstructured data, while
                                            effectively using literature information in medical informatics solutions.
                                            The workshop will focus on: </p>

                                        <p>1. Medical and health informatics-oriented data and text mining solutions
                                            that utilize relevant background knowledge from text (including scientific
                                            publications, EHRs, and database annotations). Such approaches range from
                                            term/concept recognition, association mining to event or relation
                                            extraction, i.e. gene-disease associations, protein-protein interactions,
                                            drug-drug interactions, etc. </p>

                                        <p>2. Knowledge discovery methods, that consolidate and process heterogeneous
                                            biomedical data collected from electronic bulletin boards, EHRs, scientific
                                            publications, and other types of experiments. </p>

                                        <p>3. Development of computational methods to solve a medical unmet issue via
                                            big-data analytics and machine learning approaches.</p>

                                        <p>Developing computational tools and methods mainly contribute to a better
                                            understanding of biological systems is still challenging. However,
                                            understanding of both omics data and text mining and biomedicine is
                                            essential for realizing precision medicine, tailored therapy. Thus,
                                            identification and definition of the relevant computational and biological
                                            problems, as well as development of evaluation methods for related
                                            text-mining tools are ongoing efforts.
                                        </p>
                                    </dd>
                                    <div class="more"><a><span style="margin-right: 5px;">More</span><i
                                                class="fa fa-chevron-down" aria-hidden="true"></i></a></div>
                                </li>
                                <li><i class="fa-li fa fa-desktop"></i>
                                    <dt><a href="http://dtmbio.net/dtmbio2019/" target="_blank">URL
                                            <i class="fa fa-angle-right" aria-hidden="true"></i></a></dt>
                                </li>
                            </ul>
                        </div>
                    </div>
                    <div class="col-md-12 workshop-unit" id="mostrec2019">
                        <div class="workshop-name ">
                            <h3>MoST-Rec 2019</h3>
                            <p>Workshop on Model Selection and Parameter Tuning in Recommender Systems</p>
                            <p style="font-size: 16px; margin-top: 20px; padding-left: 20px;;">
                                Room 303A, 2:00PM-5:00PM Nov 7
                            </p>
                        </div>
                        <div class="workshop-info ">
                            <ul class="fa-ul">
                                <li><i class="fa-li fa fa-users"></i>
                                    <dt>Organizer</dt>
                                    <dd>
                                        <ul>Sahin Albayrak (Berlin Institute of Technology, Germany)</ul>
                                        <ul>Defu Lian (University of Science and Technology of China, China)</ul>
                                        <ul>Fikret Sivrikaya (GT-ARC gGmbH, Germany)</ul>
                                    </dd>
                                </li>
                                <li class="workshop-desc"><i class="fa-li fa fa-bookmark"></i>
                                    <dt>Description</dt>
                                    <dd>
                                        <p>The first Workshop on Model Selection and Parameter Tuning in Recommender
                                            Systems @ CIKM 2019 is a place for discussing and exchanging recent advances
                                            and
                                            open challenges between the Model-Selection and Parameter Tuning community
                                            and
                                            Recommendation Systems community. Recommender systems have attracted strong
                                            attention of the machine learning community, especially within the last
                                            decades.
                                            Researchers have developed various algorithms proven to have good
                                            performance in
                                            the laboratory environment; however, applying them to real business cases is
                                            typically more difficult. This workshop addresses the issues of algorithm
                                            selection and parameter tuning for recommender systems. The goal is to bring
                                            together researchers from the machine learning community with the industry
                                            representatives in order to exchange information on current challenges,
                                            constraints and ideas from both domains.</p>
                                    </dd>
                                    <div class="more"><a><span style="margin-right: 5px;">More</span><i
                                                class="fa fa-chevron-down" aria-hidden="true"></i></a></div>
                                </li>
                                <li><i class="fa-li fa fa-desktop"></i>
                                    <dt><a href="http://most-rec.gt-arc.com/" target="_blank">URL
                                            <i class="fa fa-angle-right" aria-hidden="true"></i></a></dt>
                                </li>
                            </ul>
                        </div>
                    </div>
                    <div class="col-md-12 workshop-unit" id="ait2019">
                        <div class="workshop-name ">
                            <h3>AIT 2019</h3>
                            <p>Workshop on Artificial Intelligence in Transportation</p>
                            <p style="font-size: 16px; margin-top: 20px; padding-left: 20px;;">
                                Room 303B, 2:00PM-5:00PM Nov 7
                            </p>
                        </div>
                        <div class="workshop-info ">
                            <ul class="fa-ul">
                                <li><i class="fa-li fa fa-users"></i>
                                    <dt>Organizer</dt>
                                    <dd>
                                        <ul>Weinan Zhang (Shanghai Jiao Tong University)</ul>
                                        <ul>Haiming Jin (Shanghai Jiao Tong University)</ul>
                                        <ul>Lingyu Zhang (Didi Chuxing)</ul>
                                        <ul>Hongtu Zhu (Didi Chuxing; University of North Carolina at Chapel Hill)</ul>
                                        <ul>Zhenhui Jessie Li (Pennsylvania State University)</ul>
                                        <ul>Jieping Ye (Didi Chuxing; University of Michigan)</ul>
                                    </dd>
                                </li>
                                <li class="workshop-desc"><i class="fa-li fa fa-bookmark"></i>
                                    <dt>Description</dt>
                                    <dd>
                                        <p>Data-enabled smart transportation has attracted a surge of interest from
                                            machine
                                            learning and data mining researchers nowadays due to the bloom of online
                                            ride-hailing industry and rapid development of autonomous driving.
                                            Large-scale
                                            high quality route data and trading data (spatiotemporal data) have been
                                            generated every day, which makes AI an urgent need and preferred solution
                                            for
                                            the decision making in intelligent transportation systems. While a large of
                                            amount of work have been dedicated to traditional transportation problems,
                                            they
                                            are far from satisfactory for the rising need.</p>
                                        <p>We propose a half-day workshop at CIKM 2019 for the professionals,
                                            researchers, and practitioners who are interested in mining and
                                            understanding
                                            big and heterogeneous data generated in transportation, and AI applications
                                            to
                                            improve the transportation system.</p>
                                    </dd>
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                                </li>
                                <li><i class="fa-li fa fa-desktop"></i>
                                    <dt><a href="https://outreach.didichuxing.com/cikm2019/workshop/AIT/"
                                            target="_blank">URL
                                            <i class="fa fa-angle-right" aria-hidden="true"></i></a></dt>
                                </li>
                            </ul>
                        </div>
                    </div>
                    <div class="col-md-12 workshop-unit" id="hena2019">
                        <div class="workshop-name ">
                            <h3>HENA 2019</h3>
                            <p>The 3rd Workshop of Heterogeneous Information Network Analysis and Applications</p>
                            <p style="font-size: 16px; margin-top: 20px; padding-left: 20px;;">
                                Room 302B, 2:00PM-5:00PM Nov 7
                            </p>
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                        <div class="workshop-info ">
                            <ul class="fa-ul">
                                <li><i class="fa-li fa fa-users"></i>
                                    <dt>Organizer</dt>
                                    <dd>
                                        <ul>Chuan Shi (Beijing University of Posts and Telecommunications, China)</ul>
                                        <ul>Yanfang (Fanny) Ye (West Virginia University, USA)</ul>
                                        <ul>Jiawei Zhang (Florida State University, USA)</ul>
                                    </dd>
                                </li>
                                <li class="workshop-desc"><i class="fa-li fa fa-bookmark"></i>
                                    <dt>Description</dt>
                                    <dd>
                                        <p>Now we are living in an interconnected world - where most of the data,
                                            informational objects, agents, or components are interconnected or interact
                                            with each other - forming gigantic and sophisticated information networks.
                                            Most real-world applications based on information networks can be structured
                                            into heterogeneous information networks that include different types of
                                            objects or links. Recent works on heterogeneous information network analysis
                                            and its applications have led to a convergence of methodologies for network
                                            modeling, graph mining, linking analysis, data semantics mining, and
                                            incorporating classification, learning and reasoning with graphical models.
                                            As a promising network analysis paradigm, heterogeneous information network
                                            analysis also faces challenges, such as how to manage more complex
                                            heterogonous data like RDF data and how to quickly handle large-scale
                                            heterogonous information network. This workshop shall provide a forum for
                                            researchers and practitioners to share new techniques and applications in
                                            heterogonous information network analysis.</p>
                                    </dd>
                                    <div class="more"><a><span style="margin-right: 5px;">More</span><i
                                                class="fa fa-chevron-down" aria-hidden="true"></i></a></div>
                                </li>
                                <li><i class="fa-li fa fa-desktop"></i>
                                    <dt><a href="http://www.shichuan.org/HENA2019.html" target="_blank">URL
                                            <i class="fa fa-angle-right" aria-hidden="true"></i></a></dt>
                                </li>
                            </ul>
                        </div>
                    </div>
                    <div class="col-md-12 workshop-unit" id="grla2019">
                        <div class="workshop-name ">
                            <h3>GRLA 2019</h3>
                            <p>The 1st International Workshop on Graph Representation Learning and its Applications</p>
                            <p style="font-size: 16px; margin-top: 20px; padding-left: 20px;;">
                                Room 302A, 2:00PM-5:00PM Nov 7
                            </p>
                        </div>
                        <div class="workshop-info ">
                            <ul class="fa-ul">
                                <li><i class="fa-li fa fa-users"></i>
                                    <dt>Organizer</dt>
                                    <dd>
                                        <ul>Huawei Shen (Institute of Computing Technology, Chinese Academy of Sciences,
                                            China)</ul>
                                        <ul>Jian Tang (HEC Montreal and Montreal Institute for Learning Algorithms,
                                            Canada)</ul>
                                        <ul>Peng Bao (Beijing Jiaotong University, China)</ul>
                                    </dd>
                                </li>
                                <li class="workshop-desc"><i class="fa-li fa fa-bookmark"></i>
                                    <dt>Description</dt>
                                    <dd>
                                        <p>Graph structured data are ubiquitous nowadays in a variety of disciplines and
                                            domains ranging from computer science, social science, economics, medicine,
                                            to bioinformatics. Examples include social networks, knowledge graph,
                                            e-commerce networks, protein-protein interaction graphs, and molecular
                                            structures. Recently, representation learning for graphs has attracted
                                            considerable attention from researchers and communities, and led to
                                            state-of-the-art results in numerous tasks including molecule
                                            classification, new drug discovery, recommender systems, etc. This workshop
                                            aims to provide a forum for industry and academia to discuss the latest
                                            progress on graph representation learning and their applications in
                                            different fields. We expect novel research works that address various
                                            aspects of graph representation learning, including learning representations
                                            of entire graph, knowledge graph embedding, graph neural networks,
                                            applications in information network analysis, natural language
                                            understanding, recommender systems, drug discovery, and so on.</p>
                                    </dd>
                                    <div class="more"><a><span style="margin-right: 5px;">More</span><i
                                                class="fa fa-chevron-down" aria-hidden="true"></i></a></div>
                                </li>
                                <li><i class="fa-li fa fa-desktop"></i>
                                    <dt><a href="https://cikm-grla.github.io/" target="_blank">URL
                                            <i class="fa fa-angle-right" aria-hidden="true"></i></a></dt>
                                </li>
                            </ul>
                        </div>
                    </div>
                </div>
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