SAC 2014 Track on Recommender Systems: Theory and Applications (RS)
March 24 - 28, 2014, Gyeongju, Korea
The ACM Symposium on Applied Computing (SAC) has been an important venue for the past twenty-seven years, attracting applied computer scientists, computer engineers, software engineers, and application developers from around the world.
SAC 2014 is sponsored by the ACM Special Interest Group on Applied Computing (SIGAPP), and will be will held in the historic city of Gyeongju (Korea), knows as the Museum without Walls. For the second time, the ACM SAC will have a track focusing on the theory and applications of recommender systems.
The explosive growth of e-commerce environments has made the issue of information overload increasingly serious. Recommender systems aim at supporting individuals, who lack sufficient competence or resources to evaluate the potentially overwhelming number of alternatives, typically by providing a ranked list of items that are personally tailored to a user's preferences. In this sense, recommender systems are a particular type of information filtering systems that exploit users' past events (such as viewing an item) in addition to actions performed by other users.
Recommender systems have proven to be a valuable means for online users to cope with the virtual information overload and have become one of the most powerful and popular tool in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed and during the last decade, many of them have also been successfully deployed in commercial environments, such as Amazon.com, Youtube.com, Netflix, Pandora, Last.fm etc.
Development of recommender systems is a multi-disciplinary effort which involves experts from various fields such as AI, Human Computer Interaction, Information Technology, Data Mining, Statistics, Adaptive User Interfaces, Decision Support Systems, Marketing, or Consumer Behavior. Theoreticians and practitioners from these fields are continually seeking techniques to make recommender systems more efficient, cost-effective and accurate.
The aims of this track are:
1. To publish and disseminate the most important results related to recommendation systems,
2. To discuss practical issues involved in designing and deploying recommendation systems including scalability, cost of implementation, and effectiveness, and,
3. To bring together researchers involved with real-life recommendation systems and other applied computing researchers to congregate and discuss the latest problems, results and open issues in the field and exchange.
Topics of Interest
- Recommendation Algorithms
- User Studies of Recommender Systems
- Trust and Recommendations
- Preference elicitation
- Machine learning and Data Mining for Recommendation
- Explanation and justification
- Conversational Recommender Systems
- User Behavior Modeling
- Recommender Interfaces
- Security and Privacy
- Recommenders and communities
- Context-aware Recommender Systems
- Hybrid and meta recommender systems
- Group Recommendations
- Evaluation methodologies and metrics
- Preferences Elicitation
- Recommender Systems and Information Retrieval
- Ontologies and semantic web technologies
- Case Studies of real-world Recommender Systems
- Ubiquitous Computing and Recommender Systems
- Cross-Domain Recommendation
- Computational advertising
- Economic aspects and applicability
- Citation Recommendation
September 13, 2013: Submission of regular papers and SRC abstracts
November 15, 2013: Notification of paper and SRC acceptance/rejection
December 6, 2013: Camera-ready copies of accepted papers
December 13, 2013: Author registration due date
For full submission guidelines and the submission website, please follow the instructions on the SAC 2014 website carefully. Also, please note the following reminders related to paper submissions.
Authors submitting papers to the track should note that an author or a proxy attending SAC MUST present the paper. This is a requirement for the paper to be included in the ACM/IEEE digital library. No-show of scheduled papers will result in excluding the papers from the ACM/IEEE digital library.
Bracha Shapira (lead), Ben Gurion University, Israel
Lior Rokach, Ben Gurion University, Israel
Francesco Ricci, Free University of Bozen-Bolzano, Italy