Discovery Challenge 2011 (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases) opened a challenge focused on making a recommendation system for video lectures, based on historical data from the VideoLectures.Net website.
The challenge description: “This challenge is organized in order to improve the website’s current recommender system. The challenge consists of two main tasks and a “side-by” contest. The data we provided is for both of the tasks, and it is up to the contestants how it will be used for learning (building up) a recommender. Due to the nature of the problem, each of the tasks has its own merit: task 1 simulates new-user and new-item recommendation (cold-start mode), task 2 simulates clickstream based recommendation (normal mode). Data from VL.Net website does not include any explicit nor implicit user profiles. Due to the privacy-preserving constraints implicit profiles embodied in viewing sequences (clickstreams) have been transformed, so that no individual viewing sequence information can be revealed or reconstructed. There are however other viewing related data included: i) co-viewing frequencies ii) pooled viewing sequences, and iii) content related information available: lecture category taxonomy, names, descriptions and slide titles (where available), authors, institutions, lecture events and timestamps. The dataset (including the leaderboard and the test set) will remain publicly available for experimentation after the end of the challenge.”
The Prize fund of 5,500€ and ensured from the European Commission through the e-LICO EU project.