With the explosive growth of online service platforms, increasing number of people and enterprises are doing everything online. In order for organizations, governments, and individuals to understand their users, and promote their products or services, it is necessary for them to analyse big data and recommend the media or online services in real time. Effective recommendation of items of interest to consumers has become critical for enterprises in domains such as retail, e-commerce, and online media. Driven by the business successes, academic research in this field has also been active for many years. Though many scientific breakthroughs have been achieved, there are still tremendous challenges in developing effective and scalable recommendation systems for real-world industrial applications. The big data sizes and complex contextual information add further challenges to the deployment of advanced recommender systems. This workshop aims to bring together researchers with wide-ranging backgrounds to identify important research questions, to exchange ideas from different research disciplines, and, more generally, to facilitate discussion and innovation in the area of context-aware recommender systems and big data analytics.

CARS-BDA 2019 welcomes all researchers in the database, data mining, natural language processing, information retrieval to submit their original research contributions relating to all aspects of recommendation and data analytics. In particular, topics of interest for this workshop include (but are not limited to):

  • Context modeling techniques for recommender systems;
  • Context-aware user modeling for recommender systems
  • Context selection techniques for recommender systems;
  • Big data analytics techniques for recommender systems;
  • Data sets for context-dependent recommendations;
  • Algorithms for detecting the relevance of contextual data;
  • Algorithms for incorporating contextual information into recommendation process;
  • Algorithms for building explicit dependencies between contextual features and ratings;
  • Interacting with context-aware recommender systems;
  • Novel applications for context-aware recommender systems;
  • Large-scale context-aware recommender systems;
  • Evaluation of context-aware recommender systems;
  • Mobile context-aware recommender systems;
  • Context-aware group recommendations;
  • Evaluation of context-aware recommender systems.

Submissions will be reviewed by the PC members of CARS-BDA 2019. Papers will appear in the workshop proceedings, which will be publicly available online for no charge. Accepted papers will have a presentation (oral and/or poster) at the workshop. The copyright will not be transferred.

Papers must be submitted in PDF according to the new two-column ACM format published in the ACM guidelines, selecting the generic "sigconf" sample. Papers should be no more than six pages in length, including diagrams, appendices, and references.

The research paper review process is single-blind: all author names and identifying information could appear in submissions. Research papers can be submitted for review via the online submission system. Submissions are due by November 25, 2018.

Keynote Speaker:

  • Rui Zhang, The University of Melbourne

    • Bio: Rui Zhang is a Professor and leader of the Big Data and Knowledge Research Theme at the School of Computing and Information Systems of the University of Melbourne. His research interests are data mining , AI and databases. He has authored more than 100 publications in prestigious conferences and journals. He has been awarded the Future Fellowship by the Australian Research Council in 2012, Chris Wallace Award in 2015 and Google Faculty Research Award in 2017. His inventions have been adopted by major IT companies such as AT&T and Microsoft. He has been a visiting scholar in AT&T Labs-Research and Microsoft Research. He obtained his Bachelor's degree from Tsinghua University in 2001 and PhD from National University of Singapore in 2006. He regularly serves as PC members of top conferences in big data areas such as SIGMOD, VLDB, KDD, WWW Conference and ICDE.

    Title: Continuous Recommendation by Virtual Assistants via Contextual Intent Tracking

    Abstract: A new paradigm of recommendation is emerging in intelligent personal assistants such as Apple's Siri, Google Now, and Microsoft Cortana, which recommends "the right information at the right time" and proactively helps you "get things done". This type of recommendation requires precisely tracking users' contemporaneous intent, i.e., what type of information (e.g., weather, stock prices) users currently intend to know, and what tasks (e.g., playing music, getting taxis) they intend to do. Users' intent is closely related to context, which includes both external environments such as time and location, and users' internal activities that can be sensed by personal assistants. The relationship between context and intent exhibits complicated co-occurring and sequential correlation, and contextual signals are also heterogeneous and sparse, which makes modeling the context-intent relationship a challenging task. This talk discusses recent techniques for solving the challenges. The idea is to utilize collaborative capabilities among users, and learn for each user a personalized dynamic system that enables efficient nowcasting of users' intents. These new techniques significantly outperform existing ones, and provide inspiring implications for deploying large-scale proactive recommendation systems in personal assistants.

Program

  • 13:30-13:40 - Welcome
  • 13:40-14:40 - keynote Speech: Continuous Recommendation by Virtual Assistants via Contextual Intent Tracking . Rui Zhang
  • 14:40-15:00 - Research Paper 1: Loss Aversion in Recommender Systems: Utilizing Negative User Preference to Improve Recommendation Quality. Bibek Paudel, Sandro Luck and Abraham Bernstein
  • 15:00-15:30 - Coffee
  • 15:30-15:50 - Research Paper 2: Electric Big Data and Domain Analytics Based on Ontology Design. Taekeun Hong and Pankoo Kim
  • 15:50-16:10 - Research Paper 3: Fraud Detection in Stock Market via Group-aware Recommendations. Kaizhi Zhuang, Yongli Ren and Ke Deng.
  • 16:00-16:30 - Research Paper 4: D-CARS: A Declarative Context-Aware Recommender System. Rosni Lumbantoruan.
  • 16:30-16:50 - Research Paper 5: A Novel Differential Privacy Recommendation Method Based on A Distributed Framework. Ji Zhang.
  • 16:50-17:00 - Discussion

Important Dates:

  • Abstract Submission: 8 November, 201825 November, 2018
  • Full Paper Submission: 15 November, 201825 November, 2018
  • Paper Notification 1 December, 2018
  • Camera-ready deadline and copyright forms: 14 December, 2018
  • Workshop at WSDM 2019: 15 February, 2019

Workshop organizers:

PC co-chairs

Xiangmin Zhou, RMIT University, Australia, xiangmin.zhou@rmit.edu.au (primary contact person)
Ji Zhang, University of Southern Queensland, Australia, Ji.Zhang@usq.edu.au
Yanchun Zhang, Victoria University, Australia, yanchun.zhang@vu.edu.au