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RecSys 2016 : 10th ACM Conference on Recommender SystemsConference Series : Conference on Recommender Systems | |||||||||||
Link: http://recsys.acm.org/recsys16/ | |||||||||||
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Call For Papers | |||||||||||
Call for Contributions
Papers Tutorials Workshops Doct. Symp. Demos Posters Call for Papers We are pleased to invite you to participate in the Tenth ACM Conference on Recommender Systems (RecSys 2016), the premier venue for research and applications of recommendation technologies. The tenth RecSys will be held in Boston, Massachusetts, USA on the MIT and IBM Research campuses from Sept 15th to Sept 19th, 2016. The conference will continue RecSys’ practice of connecting the research and practitioner communities to exchange ideas, frame problems, and share solutions. This year marks the tenth anniversary of RecSys, and the conference will feature events and a new track entitled Past, Present and Future to celebrate ten years of RecSys. The track invites submissions that reflect on past, present, and future research foci in the field of recommender systems, along with reflections on the role which RecSys played in shaping our research agendas. All proceedings will be published one week before the first day of the conference. We construe recommender systems broadly, including applications ranging from e-commerce to social networking, platforms from web to mobile and beyond, and a wide variety of technologies ranging from collaborative filtering to knowledge-based reasoning. Topics of interest for RecSys 2016 include (but are not limited to): Algorithm scalability Case studies of real-world implementations Conversational recommender systems Context-aware recommenders Evaluation metrics and studies Explanations and evidence Field and user studies Group recommenders Impact studies Innovative/New applications Machine learning for recommendation Mobile and multi-channel recommendations Novel paradigms Personalization Preference elicitation Privacy and Security Recommendation algorithms Social recommenders Semantic technologies for recommendation Targeted advertising Trust and reputation Theoretical foundations User interaction and interfaces User modeling This year authors will be asked to select whether their paper is primarily Algorithm or Application focussed. This will allow us to better assign reviewers and to balance the program. There are three paper submission categories: LONG PAPER submissions should report on substantial contributions of lasting value. The maximum length is 8 pages. Each accepted long paper will be included in the conference proceedings and presented in a plenary session as part of the main conference program. We expect the review process to be highly selective: the acceptance rates for full papers in the past three years were 20-24%. SHORT PAPER submissions typically discuss exciting new work that is not yet mature enough for a long paper. The maximum length is 4 pages. Each accepted short paper will be included in the conference proceedings and presented in a poster session. The presentation may include a system demonstration. Note that rejected long paper submissions will not be considered as short papers. PAST, PRESENT, AND FUTURE PAPER submissions celebrate the innovation and growth of the community, put forward innovative future applications or provide material that helps set a future research agenda for the Recommender System community. To mark the tenth year of the RecSys Conference, this track encourages papers that consider a broad perspective on how the field has evolved and the challenges and directions that lay ahead. We encourage, position papers, both qualitative and quantitative historical analyses, reflections on persistent or fleeting trends in the field and blue sky future agendas for recommender research. Papers can be up to 4 pages in length and will be judged on originality and creativity. Each accepted Past, Present, Future paper will be included in the conference proceedings and presented in brief entertaining oral presentations at one of the birthday events or technical sessions. |
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