2010 Challenge on Context-aware Movie Recommendation
Participants were expected to submit papers, experimental results, and an additional alorithmic onepager describing their solution. The quality of all three was then used to crown the winner of the challenge. The overall winner was Mining Mood-specific Movie Similarity with Matrix Factorization for Context-aware Recommendation - Yue Shi, Martha Larson, Alan Hanjalic.
Track 1 - Weekly Recommendation
The Weekly Recommendation track focused on recommending movies for the Christmas 2009 week and the Oscars' 2010 week in two datasets, both released exclusively for the challenge. One from Moviepilot and one from Filmtipset.
Track 2 - Mood-based Recommendation
The mood-based recommendation track focused on recommending movies with a specific emotion keyword. The dataset used was an anonymized subset of the data collected by Moviepilot.
Track 3 - Social Recommendation
The social recommendation track focused on recommending movies based on users' social connections, the dataset was an anonymized subset of the data collected by Filmtipset.
Winning submission: Adapting Neighborhood and Matrix Factorization Models for Context Aware Recommendations - Nathan N. Liu
A final evaluation was conducted in which the participants were asked to perform recommendations for a selected number of users for the context end of september (the week of the RecSys2011 week). The first few movies in each recommended list were sent to users of Moviepilot's service who had signed up for this. Each of these users were asked to answer the following questions:
- Was it a good recommendation?
- Was it a surprising recommendation?
- Did the movie match your expectations?
- Do you think you would have watched the movie eventually, even if we hadn’t recommended it to you?
ACM TIST Special Issue
Extended versions of some of the papers accepted to the workshop will be available in an ACM TIST Special issue.
- Challenge Website
- Proceedings of the 2010 Challenge on Context-aware Movie Recommendation at the ACM Digital Library