The MovieLens datasets, first released in 1998, describe people’s expressed preferences for movies. These preferences take the form of tuples, each the result of a person expressing a preference (a 0-5 star rating) for a movie at a particular time. These preferences were entered by way of the MovieLens web site1 — a recommender system that asks its users to give movie ratings in order to receive personalized movie recommendations. Source: The MovieLens Datasets: History and Context Image Source: http://files.grouplens.org/papers/harper-tiis2015.pdf
Children’s Book Test (CBT) is designed to measure directly how well language models can exploit wider linguistic context. The CBT is built from books that are freely available thanks to Project Gutenberg. Source: CBT Image Source: https://research.fb.com/downloads/babi/
The iPinYou Global RTB(Real-Time Bidding) Bidding Algorithm Competition is organized by iPinYou from April 1st, 2013 to December 31st, 2013.The competition has been divided into three seasons. For each season, a training dataset is released to the competition participants, the testing dataset is reserved by iPinYou. The complete testing dataset is randomly divided into two parts: one part is the leaderboard testing dataset to score and rank the participating teams on the leaderboard, and the other part is reserved for the final offline evaluation. The participant's last offline submission is evaluated by the reserved testing dataset to get a team's offline final score. This dataset contains all three seasons training datasets and leaderboard testing datasets.The reserved testing datasets are withheld by iPinYou. The training dataset includes a set of processed iPinYou DSP bidding, impression, click, and conversion logs. Source: iPinYou Global RTB Bidding Algorithm Competition Dataset Image Source: http://contest.ipinyou.com/ipinyou-dataset.pdf
TripClick is a large-scale dataset of click logs in the health domain, obtained from user interactions of the Trip Database health web search engine. Provide: * Approximately 5.2 million user interactions * IR evaluation benchmark * Trainin data for deep learning IR models
The task is to predict the chances of a user listening to a song repetitively after the first observable listening event within a time window was triggered. If there are recurring listening event(s) triggered within a month after the user's very first observable listening event, its target is marked 1, and 0 otherwise in the training set. KKBox provides a training data set consists of information of the first observable listening event for each unique user-song pair within a specific time duration. Metadata of each unique user and song pair is also provided. The train and the test data are selected from users listening history in a given time period, and are split based on time. Note that only the labeled train set of the dataset is used for benchmarking.