CARS- 2010 - 2nd Workshop on Context-Aware Recommender Systems & Challenge and Workshop on Context-Aware Movie Recommendation

Website recsys.acm.org/2010 | Edit Freely

Category CARS- 2010

Deadline: June 26, 2010 | Date: September 26, 2010

Venue/Country: Barcelona, Spain

Updated: 2010-06-04 19:32:22 (GMT+9)

Call For Papers - CFP

Gediminas Adomavicius

Alexander Tuzhilin

Shlomo Berkovsky

Ernesto William De Luca

Alan Said

September 26 and September 30,

CARS: Workshop Web Site.

CAMRa: Workshop Web Site.

The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including e-commerce, personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. While a substantial research has already been performed in the area of recommender systems, the vast majority of existing approaches focuses on recommending the most relevant items to users and does not take into account additional contextual information, such as time, location, weather, or the company of other people. Therefore, this joint workshop and challenge aim 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 (CARS) and tackle practical challenges of context-aware movie recommendation (CAMRa).

CARS-2010 builds on the success of the first Workshop on Context-Aware Recommender Systems (CARS-2009) and will focus on the general issues encountered in context-aware recommendations, including novel research approaches, promising research directions, and important practical applications.

CAMRa is set up as a challenge, where representatives from research and industry working on context-aware movie recommenders, will be able to exchange ideas and results. Two datasets, one from Moviepilot and one from Filmtipset, will be released. The datasets contain a number of contextual features, typically not found in standard collaborative filtering datasets, i.e., social network, intended audience, mood, etc. The challenge focuses on classification and ranking accuracy metrics of context-aware recommendation algorithms for movies. The participating teams will use one or more of the additional contextual features to generate context-aware recommendations. CAMRa submissions are expected to focus on the challenge and algorithms evaluated using the released datasets and will be reviewed by a panel of distinguished researchers.