Abstract

The challenge and workshop on Context-Aware Movie Recommendation (CAMRa2010) were conducted jointly in 2010 with the Recommender Systems conference. The challenge focused on three context-aware recommendation scenarios: time-based, mood-based, and social recommendation. The participants were provided with anonymized datasets from two real-world online movie recommendation communities and competed against each other for obtaining the highest accuracy of recommendations. The datasets contained contextual features, such as tags, annotation, social relationsips, and comments, normally not available in public recommendation datasets. More than 40 teams from 21 countries participated in the challenge. Their participation was summarized by 10 papers published by the workshop, which have been extended and revised for this special section. In this preface we overview the challenge datasets, tasks, evaluation metrics, and the obtained outcomes.

@article{Said:2013:ISS:2414425.2414438,
 author = {Said, Alan and Berkovsky, Shlomo and De Luca, Ernesto W.},
 title = {Introduction to special section on CAMRa2010: Movie recommendation in context},
 journal = {ACM Trans. Intell. Syst. Technol.},
 issue_date = {January 2013},
 volume = {4},
 number = {1},
 month = feb,
 year = {2013},
 issn = {2157-6904},
 pages = {13:1--13:9},
 articleno = {13},
 numpages = {9},
 url = {http://doi.acm.org/10.1145/2414425.2414438},
 doi = {10.1145/2414425.2414438},
 acmid = {2414438},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Recommender systems, context-aware recommendations, context-awareness, datasets, social network analysis, user modeling},
}