News « Challenge on Context-aware Movie Recommendation

Final Evaluation

These are the final evaluation results.

For track 1 where really wasn’t much results as all submitted results were close to 0.

Track 2 was somewhat better, the results are the number of erroneous matchings of household member and rating:

Yancui Shi, Xiangwu Meng and Licai Wang. A heuristic approach to identifying the specific household member for a given rating – 1303
Jose Bento, Nadia Fawaz, Andrea Montanari and Stratis Ioannidis. Identifying Users From Their Rating Patterns - 58
Pedro G. Campos, Fernando Díez and Alejandro Bellogin. Temporal Rating Habits: A Valuable Tool for Rater Differentiation – 60
Yue Shi, Martha Larson and Alan Hanjalic. Mining Relational Context-aware Graph for Rater Identification - 107
Claudio Biancalana, Fabio Gasparetti, Alessandro Micarelli, Alfonso Miola and Giuseppe Sansonetti. Context-aware Movie Recommendation based on Signal Processing and Machine Learning - 333

Keynote

We are please to announce that Alexander Tuzhilin will be giving the opening keynote at the CAMRa workshop.

Alexander Tuzhilin is Professor of Information Systems at the Stern School School of Business at New York University

Title
Context-Aware Recommender Systems: The NextFrontier

Abstract
The field of Context-Aware Recommender Systems (CARS) has experienced significant growth over the last several years. Still, despite all these developments, it still remains a young and an emerging field where many issues and problems have been formulated only very recently and, therefore,have been barely explored. In this talk, I will focus on some of the challenges and issues that, in my opinion, are critical for the successful future development of the CARS field. I will present these challenges and discuss some ideas and possible initial approaches on how to address them.