An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
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Public movie rating datasets, like MovieLens or Netflix, have long been popular and widely used in the recommmender systems domain for comparison and experimentation. More and more however these datasets are becoming outdated and fail to incorporate new and relevant items. In our work, we tap into the vast availability of social media and construct a new movie rating dataset 'MovieTweetings' based on public and well-structured tweets. New data is added on a daily basis, which guarantees the MovieTweetings dataset to always incorporate ratings on the newest and most relevant movies.