Recommender systems are personalized: we expect the results given to a particular user to reflect that user's preferences. Some researchers have studied the notion of calibration, how well recommendations match users' stated preferences, and bias disparity the extent to which mis-calibration affects different user groups. In this paper, we examine bias disparity over a range of different algorithms and for different item categories and demonstrate significant differences between model-based and memory-based algorithms.

The system is interpretable and user friendly and does not require labeled training data, hence can be rapidly and cost-effectively used across different domains in applied setups. The system flow includes three stages: First, it generates domain-specific aspect and opinion lexicons based on an unlabeled dataset; second, it enables the user to view and edit those lexicons (weak supervision); and finally, it enables the user to select an unlabeled target dataset from the same domain, classify it, and generate an aspect-based sentiment report. ABSApp has been successfully used in a number of real-life use cases, among them movie review analysis and convention impact analysis.

It is becoming increasingly clear that the big tech giants such as Google, Facebook, and Microsoft are extremely generous with their latest machine learning algorithms and packages (they give those away freely) because the entry barrier to the world of algorithms is pretty low right now. The open source community and tools (such as scikit-earn) have come a long way, and plenty of open source initiatives are propelling the vehicles of data science, digital analytics, and machine learning. Standing in 2019, we can safely say that algorithms, programming frameworks, and machine learning packages (or even tutorials and courses how to learn these techniques) are not the scarce resource but high-quality data is. This often becomes a thorny issue on the side of the practitioners in data science (DS) and machine learning (ML) when it comes to tweaking and fine-tuning those algorithms. It will also be wise to point out, at the very beginning, that the current article pertains to the scarcity of data for algorithmic investigation, pedagogical learning, and model prototyping.

In June 2002, Steven Spielberg premiered a new movie he had directed, Minority Report, based on a famous 1956 short story by the science fiction writer Philip K. Dick. Set in 2054 in a crime -free Washington, DC, the film stars Tom Cruise, who plays the head of Precrime--an elite police unit that arrests killers before they commit their crimes. The team has the authority to make its arrests based on the visions of three clairvoyant individuals who can see into the future. But soon Cruise is evading his own unit--in a city where everyone and everything is tracked--when the psychics predict he will commit a murder of his own. More than 15 years later, this approach to law enforcement happily seems far -fetched.

This VS Code extension will enable you to use DeepCode AI to identify bugs and issues in Visual Studio Code.

Taylor Swift's lawyers threatened to sue Microsoft over the company's Tay chatbot. The Guardian reports that a new book by Microsoft president Brad Smith reveals lawyers for Taylor Swift weren't happy with the company using the name Tay for its chatbot. Microsoft's chatbot was originally designed to hold conversations with teenagers over social media networks, but Twitter users turned it into a racist chatbot in less than a day. Smith checked his emails during a vacation and found out that Taylor Swift's team was demanding a name change for the Tay chatbot. "An email had just arrived from a Beverly Hills lawyer who introduced himself by telling me: 'We represent Taylor Swift, on whose behalf this is directed to you.'"