The challenge for the hackathon is to build with better more accurate predictive models of metropolitan air pollution. The EPA’s Air Quality Index is used daily by people suffering from asthma and other respiratory diseases to avoid dangerous levels of outdoor air pollutants, which can trigger attacks. According to the World Health Organisation there are now estimated to be 235 million people suffering from asthma. Globally, it is now the most common chronic disease among children, with incidence in the US doubling since 1980. The model we build could be used as the basis for an early warning system that is capable of accurately predicting dangerous levels of air pollutants on an hourly basis.
The ability to search literature and collect/aggregate metrics around publications is a central tool for modern research. Both academic and industry researchers across hundreds of scientific disciplines, from astronomy to zoology, increasingly rely on search to understand what has been published and by whom.
Microsoft Academic Search is an open platform that provides a variety of metrics and experiences for the research community, in addition to literature search. It covers more than 50 million publications and over 19 million authors across a variety of domains, with updates added each week. One of the main challenges of providing this service is caused by author-name ambiguity. On one hand, there are many authors who publish under several variations of their own name. On the other hand, different authors might share a similar or even the same name.
As a result, the profile of an author with an ambiguous name tends to contain noise, resulting in papers that are incorrectly assigned to him or her. This KDD Cup task challenges participants to determine which papers in an author profile were truly written by a given author.
Predict which BestBuy product a mobile web visitor will be most interested in based on their search query or behavior over 2 years
Predict when a caller might would make an outgoing call based on mobile usage data
The challenge is to recommend missing links in a social network. Participants will be presented with an external anonymized, directed social graph (no, not Facebook, keep guessing) from which some edges have been deleted, and asked to make ranked predictions for each user in the test set of which other users they would want to follow.
The challenge is to detect when a comment from a conversation would be considered insulting to another participant in the conversation. Samples could be drawn from conversation streams like news commenting sites, magazine comments, message boards, blogs, text messages, etc.
The idea is to create a generalizable single-class classifier which could operate in a near real-time mode, scrubbing the filth of the internet away in one pass.