* We apologize if you receive multiple copies of this CfP * * For the online version of this Call, visit: https://recsys.acm.org/recsys19/call/tutorials *
RecSys 2019 is pleased to invite proposals for tutorials to be given in conjunction with the conference. The goal of the tutorials is to provide conference attendees, including early-career researchers and researchers crossing-over from related disciplines, with an opportunity to learn about recommender system concepts and techniques. Tutorials also serve as a venue to share presenters’ expertise with the global community of recommender system researchers and practitioners. Tutorials focus on specific topics including, but not limited to: * Introductions to recommender systems or to specific techniques (e.g., deep learning, feature engineering, tensorflow), * Evaluation of recommender systems (e.g., system-centric and user-centric evaluation, experimentation), * Context-aware (including location-based) recommender systems, * Designing user experiences and interactions (e.g., virtual assistants, chatbots, etc.), * Using different types of data (semantic web, graphs,) and media (text, images, video, speech) for building recommendations, * Ethical and legal aspects of recommender systems (e.g., privacy, fairness, accountability, transparency, and control of bias), * Recommender systems facing real-world challenges (e.g., large-scale recommender systems or stream-based recommendation), * Building and deploying recommender systems in specific domains (e.g., music, tourism, education, TV/video, jobs, enterprise, health, and/or fashion), * Recommender systems supporting decision making, * Recommendation for groups, tasks, or situations, including intent-aware recommender systems, * Eliciting and learning user preferences, * Recommender systems that take users’ emotional state, physical state, personality, trust, level-of-expertise, and/or cognitive readiness into account, * Sensors and recommender systems (including mobile recommender systems and wearables), * Intersections of recommender systems with other domains (e.g., information retrieval, machine learning, human computer interaction, or databases). * Recommender systems in new domains, such as e-government, smart cities and energy. The length of your proposed tutorial should be commensurate with the presented materials and the projected interest of the RecSys community in the tutorial topic. We may work with accepted tutorial presenters to adjust the length of the tutorial, considering that tutorials may use up to two 90-minute slots, i.e. the length of the tutorials will be either 90 or 180 minutes. We actively encourage both researchers and industry practitioners to submit tutorial proposals that target different levels of expertise and different interests. We also encourage the submission of hands-on tutorials, for instance through the use of notebooks that combine theoretical concepts with practical exercises. PROPOSAL FORMAT AND SUBMISSION The tutorial proposal should be a PDF document no more than 2 pages long, submitted by e-mail to tutorials2...@recsys.acm.org and organized as follows: * Tutorial title. * Tutorial length. * Motivation for proposing this tutorial (why is it important for RecSys). * Name, email address, and affiliation of tutorial instructor(s). Each listed presenter must present in person at the conference. * Detailed bulleted outline of the tutorial (this point should take the most space). * Targeted audience (introductory, intermediate, advanced) and prerequisite knowledge or skills. * Importance of the topic for the RecSys community. * Teaching experiences and history of prior tutorials by the presenter(s). * List of relevant publications by the presenter(s). The following elements are not mandatory for the proposal, but encouraged: * A short explanation of relationship of the tutorial proposal to “trends” at past RecSys conferences. * A 2-minute video where the presenters introduce themselves and pitch their tutorial. * Statement that the materials (slides, readings, and/or code) used/mentioned in the tutorial will be publicly available after the tutorial. * Notebooks (e.g. iPython or Jupyter) or other interactive code that will be used during the course, if any. EVALUATION CRITERIA Tutorial proposals will be reviewed according to: ability of the tutorial to contribute to strengthening the foundations of recommender system research, or to broadening the field to look at important new challenges and techniques, experience and skill of the presenter(s), and the value of any materials released with the tutorial for the community. IMPORTANT DATES * Tutorial proposal submission deadline: May 16th, 2019 * Tutorial proposal notification: June 1st, 2019 * Camera-ready tutorial summary deadline: July 22nd, 2019 Deadlines refer to 23:59 (11:59pm) in the AoE (Anywhere on Earth) time zone. TUTORIAL CHAIRS * Alejandro Bellogín, Autonomous University of Madrid, Spain * Denis Parra, Pontificia Universidad Católica, Chile _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
