In the war for top software talent, hiring high-quality machine learning engineers is a challenging technical problem in of itself.

The company's new policy was first reported by The Washington Post. Facebook was heavily criticized last year for refusing to take down an altered video of Speaker Nancy Pelosi that had been edited to make it appear as though she was slurring her words. At the time, the company defended its decision, saying it had subjected the video to its fact-checking process and had reduced its reach on the social network. It did not appear that the new policy would have changed the company's handling of the video with Ms. Pelosi. The announcement comes ahead of a hearing before the House Energy & Commerce Committee on Wednesday morning, during which Ms. Bickert, Facebook's vice president of global policy management, is expected to testify on "manipulation and deception in the digital age," alongside other experts. Because Facebook is still the No. 1 platform for sharing false political stories, according to disinformation researchers, the urgency to spot and halt novel forms of digital manipulation before they spread is paramount.

Unless you've been living under a rock the past 10-plus years, you've likely heard about the enormous difficulties facing the newspaper industry, particularly local journalism, which was once the backbone of communities around the world. The past year resulted in the worst year of job losses at news organizations since 2009 -- the year the recession sent reporters into new careers. Pinnacles of regional journalism -- the place where many reporters spent their entire careers -- are gone. The losses include the fire sale of the entire Knight Ridder chain to McClatchy, the demise of venerable newspapers like The Oakland Tribune in California and more. For years, a website called Newspaper Death Watch tracked each individual newspaper closure.

When I was a little younger – okay, I mean, a lot younger – the year 2020 seemed so far away. It was a period of nothing more than a wonder of possibilities, since such a time was nothing more than science fiction to me as a child. Films and television were filled with speculation as to what the year 2020 might bring – there were flying cars and hover boards, talking robots and the ease of commuting from Earth to Mars all seemed so real and tangible. I always wondered what marvels the future would bring, yet the harsh reality of today is that, here in the UK, we're reeling in the wake of a pre-Christmas election and are still coming to terms with Britain's exit from the European Union. Yes, the start of 2020 looks bloody depressing, yet it all seemed so different when I was 10.

Existing Recommender Systems mainly focus on exploiting users' feedback, e.g., ratings, and reviews on common items to detect similar users. Thus, they might fail when there are no common items of interest among users. We call this problem the Data Sparsity With no Feedback on Common Items (DSW-n-FCI). Personality-based recommender systems have shown a great success to identify similar users based on their personality types. However, there are only a few personality-based recommender systems in the literature which either discover personality explicitly through filling a questionnaire that is a tedious task, or neglect the impact of users' personal interests and level of knowledge, as a key factor to increase recommendations' acceptance. Differently, we identifying users' personality type implicitly with no burden on users and incorporate it along with users' personal interests and their level of knowledge. Experimental results on a real-world dataset demonstrate the effectiveness of our model, especially in DSW-n-FCI situations.