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Put a battery-operated talking unicorn in your online shopping cart, and you may get an alert suggesting some AA cells to juice up the conversation. Binge-watch a few action movies and you may see titles of martial arts cinema fill up your must-see list.<\/p>\n\n\n\n
Recommendation engines try to discern habits, likes, and other affinity traits to anticipate what you may need or want based on past actions. News consumption can fall into these patterns: We know for instance that people go to search engines to find out more about a story (opens in new tab)<\/span><\/a>, be it more background or further developments. Certain types of news and feature stories lend itself to typical user behaviors: An impending hurricane, for instance, triggers preparation research (if you\u2019re in the path), offers of help to donate supplies or blood (if you\u2019re nearby) and historical curiosity of past hurricanes. Even celebrity stories inspire certain common impulses: An engagement announcement will launch some to seek a peek at the ring, others to check out past (failed) relationships.<\/p>\n\n\n\n How might a news recommendation system offer more stories, yet not fall into the trap of filter bubbles and echo chambers? The first thing is, you need a high-quality benchmark dataset. That\u2019s where MIND comes in: a mammoth collection of anonymized data from user behavior logs of about 1 million people. Few companies in the world attract those kinds of numbers, and Microsoft News is one of them.<\/p>\n\n\n\n When you work at the scale that Microsoft News does \u2014 in 140 countries around the world \u2014 the challenge is not to overload its half billion readers. In the not-so-distant past, newspaper print space and radio & TV time constrained how news was reported, displayed and ranked. When the Internet smashed conventions of information delivery, audiences had to assume the responsibility for their own news diet. That diet can be hard to maintain when thousands of news stories come at people every day \u2014 especially when you throw in social media.<\/p>\n\n\n\n And while people want to seek news on their own, they also expect that information relevant to their locality (local news), habits and interests will find their way. The challenge becomes shared amongst the newsroom creators, the distribution channels and the readers.<\/p>\n\n\n\n Our data scientists and engineering team behind MIND presented this dataset at the prestigious Association for Computation Linguistics 2020 conference (opens in new tab)<\/span><\/a>. There, they covered their white paper, MIND: A Large-scale Dataset for News Recommendation<\/a>.<\/p>\n\n\n\n Like all researchers, they\u2019re eager to share: The team has issued a call out to the science world and news publisher to dive into this dataset and come up with ways to rank news articles that align with users\u2019 interests. How might the dataset be used to study the performance of different kinds of models? Might metrics be to evaluate a recommendation\u2019s effectiveness include diversity, \u201cpolitical\u201d balance, a dose of serendipity?<\/p>\n\n\n\n And just to make it interesting, #cashprizes. Grand prize is $10,000, with two second-place prizes at $3,000 and four third-place prizes at $1,000.<\/p>\n\n\n\n Competition registration (opens in new tab)<\/span><\/a> opens July 20, after which participants can download the training dataset to test their algorithms. The actual competition starts August 21, when the real test data will be made available. Technical reports are encouraged but not required, although the winning entries will be published online and presented at a future workshop. The deadline is midnight September 4 (UTC).<\/p>\n\n\n\n The dataset is free to download for research purposes on MIND Website (opens in new tab)<\/span><\/a>, and baseline algorithms are available on Microsoft Recommenders repository (opens in new tab)<\/span><\/a>. There are two offerings of the dataset: the whole kit\u2019n\u2019caboodle and a smaller subset of 50,000 users called MIND-small, but the goals the Microsoft data scientists aim to achieve aren\u2019t small-minded at all.<\/p>\n\n\n\nLet the MIND Games begin<\/h2>\n\n\n\n