Mining cross-domain rating datasets from structured data on twitter

While rating data is essential for all recommender systems research, there are only a few public rating datasets available, most of them years old and limited to the movie domain. With this work, we aim to end the lack of rating data by illustrating how vast amounts of ratings can be unambiguously collected from Twitter. We validate our approach by mining ratings from four major online websites focusing on movies, books, music and video clips. In a short mining period of 2 weeks, close to 3 million ratings were collected. Since some users turned up in more than one dataset, we believe this work to be amongst the first to provide a true cross-domain rating dataset.