- Each paper has 15 minutes to be presented, and additional 5 minutes for questions.
- You need to visit the ACM SAC 2017 website to learn the programs and schedules for your presentation, http://www.sigapp.org/sac/sac2017/program17.htm
- We are not going to release the programs for our track. The schedule of the presentation or program will be released by the ACM SAC 2017 Conference. You can refer to the conference website to learn more information, such as VISA inviation letter, registration, conference venue, schedule and programs, etc.
- Conference Registration (URL): Author Registration (credit card and Bank Transfer) must be completed by Friday December 16, 2016 in order to ensure paper/poster inclusion in the conference proceedings. Please note that the discounted Student Registration rate does NOT cover paper or poster inclusion in the conference proceedings. Student registration is intended to encourage student attendance and participation. Student registration includes one banquet ticket.
- The overall acceptance rate (based on all tracks) is 24% at ACM SAC 2017
- A list of accepted papers:https://recsystrack.wordpress.com/accepted-papers/
- Submission system has been closed. We have received 43 valid submissions this year! The acceptance notification will be sent out around Nov 18, 2016. Thanks again for your contributions!
- Submission deadline has been extended to
October 7th, 2016
- The ACM SAC 2017 Conference dates were changed to be April 3-7, 2017
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 32nd ACM Symposium on Applied Computing (ACM SAC 2017) will be held in Marrakech, Morocco during April 3-7, 2017. 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.
Track on Recommender Systems: Theory and Applications at ACM SAC 2014, Gyeongju, Korea
Track on Recommender Systems: Theory and Applications at ACM SAC 2013, Coimbra, Portugal