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  • Each accepted full paper will have 20+5 minutes — 20 minutes for presentations and 5 minutes for QA. For more details, please refer to the program at ACM SAC 2018 website. The short paper will be presented as posters. The guideline for you to design the poster is posted at https://www.sigapp.org/sac/sac2018/posters.html
  • For the final program and presentation details, the authors should find such information from the official ACM SAC 2018 website
  • The acceptance/rejection notifications have been sent out. We received 37 valid submissions, and finally accepted 8 long papers and 2 short papers. For the information about camera-ready submissions and conference registration, please refer to the official website of ACM SAC 2018 conference.
  • This year, we received 37 valid submissions. The paper bidding and reviewing process will start soon
  • The submission deadline is extended to Sep 25, 2017
  • 07/19/2017, Full paper is limited to 10 pages: 8 pages included in registration fee plus up to 2 pages with additional fees
  • 05/23/2017, Our track was accepted by ACM SAC 2018
  • 05/05/2017, We submitted the track proposal to ACM SAC 2018

ACM SAC

The ACM Symposium on Applied Computing is recognized as a primary forum for applied computer scientists and application developers from around the world to interact and present their work. The 33rd ACM Symposium on Applied Computing (ACM SAC 2018) will be held in Pau, France, April 9 – 13, 2018. It is sponsored by the ACM Special Interest Group on Applied Computing (SIGAPP) and is presented in cooperation with other ACM Special Interest Groups.

Track on Recommender Systems

With the development of information technologies, human beings are more and more surrounded with floods of information, which further results in problems a person can have in understanding an issue or making decisions that can be caused by the presence of too much information. Recommender systems (RecSys) have proven to be helpful in alleviating this information overload problem, providing personalized services and assisting users’ decision making. The basic idea behind RecSys is to infer users’ tastes from their past behaviors (such as user ratings, purchases, reviews, click-throughs, etc). RecSys have been widely applied in a number of areas, including eCommerce (e.g., Amazon, eBay), movies (e.g., Netflix, Moviepilot), music (e.g., Pandora, Spotify), news (e.g., Yahoo news), tags (e.g., Flickr), social media (e.g., Twitter), online education (e.g., Coursera), and so forth.

The development of RecSys promotes various research topics, such as user interaction and interfaces, algorithm design and evaluations, computational efficiency, and recommendation explanations. As one of applied sciences, the field of recommender systems attracts experts and receives contributions from multidisciplinary areas, including Artificial Intelligence, Human Computer Interaction, Data Science, Decision Support Systems, Marketing, etc.

This track aims to provide a dedicated forum to researchers in RecSys and other applied computing areas for discussing the open research problems, solid solutions, latest challenges, novel applications and innovative research approaches in RecSys.

History

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