Listen to Slate Money via Apple Podcasts, Overcast, Spotify, Stitcher, or Google Play. On this week's episode, Emily Peck, Felix Salmon, Anna Szymanski, and Jordan Weissmann discuss: In the Slate Plus segment: Is Billions or Succession the best financial show on TV? Slate Plus members: Get your ad-free podcast feed. Emily Peck is a senior reporter at HuffPost. Felix Salmon is a journalist. Anna Szymanski is a corporate consultant who previously worked in emerging-markets investment.
Films that wrestle with the rapidly changing nature of war, though, are rarer. As drone warfare continues its slow march into public consciousness, Eye in the Sky is the best movie yet to tackle the legal and moral quagmire surrounding modern technological warfare. To do that, Eye in the Sky goes granular, telling the story of one particular mission on one particular day. In the movie, opening wide today, British colonel Katherine Powell (Helen Mirren) oversees a secret operation to capture a terrorist cell in Nairobi, Kenya. When the mission uncovers a more immediate threat than anticipated, though, the situation escalates.
Former New York State Homeland Security Director Michael Balboni says Russians have used American technology against the U.S. Operatives of the Kremlin-linked troll farm called the Internet Research Agency reportedly created Twitter accounts pretending to be local newspapers -- and shared real local stories rather than fake news. According to NPR, at least 48 separate Twitter accounts were created well before the 2016 presidential election and were designed to look like legitimate city newspapers. In some cases, they used names of newspapers from the past, such as the Chicago Daily News, which folded in 1978. The accounts, some of which gathered nearly 20,000 followers, didn't purposely spread false news and instead shared credible local news stories without any particular slant. NPR notes that the plan for such accounts was to create trust among media consumers before starting to infuse misinformation into its shared posts.
Current recommender systems exploit user and item similarities by collaborative filtering. Some advanced methods also consider the temporal evolution of item ratings as a global background process. However, all prior methods disregard the individual evolution of a user's experience level and how this is expressed in the user's writing in a review community. In this paper, we model the joint evolution of user experience, interest in specific item facets, writing style, and rating behavior. This way we can generate individual recommendations that take into account the user's maturity level (e.g., recommending art movies rather than blockbusters for a cinematography expert). As only item ratings and review texts are observables, we capture the user's experience and interests in a latent model learned from her reviews, vocabulary and writing style. We develop a generative HMM-LDA model to trace user evolution, where the Hidden Markov Model (HMM) traces her latent experience progressing over time -- with solely user reviews and ratings as observables over time. The facets of a user's interest are drawn from a Latent Dirichlet Allocation (LDA) model derived from her reviews, as a function of her (again latent) experience level. In experiments with five real-world datasets, we show that our model improves the rating prediction over state-of-the-art baselines, by a substantial margin. We also show, in a use-case study, that our model performs well in the assessment of user experience levels.