In many personalised recommendation problems, there are examples of items users prefer or like, but no examples of items they dislike. A state-of-the-art method for such implicit feedback, or one-class collaborative filtering (OC-CF), problems is SLIM, which makes recommendations based on a learned item-item similarity matrix. While SLIM has been shown to perform well on implicit feedback tasks, we argue that it is hindered by two limitations: first, it does not produce user-personalised predictions, which hampers recommendation performance; second, it involves solving a constrained optimisation problem, which impedes fast training. In this paper, we propose LRec, a variant of SLIM that overcomes these limitations without sacrificing any of SLIM's strengths.At its core, LRec employs linear logistic regression; despite this simplicity, LRec consistently and significantly outperforms all existing methods on a range of datasets. Our results thus illustrate that the OC-CF problem can be effectively tackled via linear classification models.

As text classifiers become increasingly used in real-time applications, it is critical to consider not only their accuracy but also their robustness to changes in the data distribution. In this paper, we consider the case where there is a confounding variable Z that influences both the text features X and the class variable Y. For example, a classifier trained to predict the health status of a user based on their online communications may be confounded by socioeconomic variables. When the influence of Z changes from training to testing data, we find that classifier accuracy can degrade rapidly. Our approach, based on Pearl's back-door adjustment, estimates the underlying effect of a text variable on the class variable while controlling for the confounding variable. Although our goal is prediction, not causal inference, we find that such adjustments are essential to building text classifiers that are robust to confounding variables. On three diverse text classifications tasks, we find that covariate adjustment results in higher accuracy than competing baselines over a range of confounding relationships (e.g., in one setting, accuracy improves from 60% to 81%).

Clickbaits are articles with misleading titles, exaggerating the content on the landing page. Their goal is to entice users to click on the title in order to monetize the landing page. The content on the landing page is usually of low quality. Their presence in user homepage stream of news aggregator sites (e.g., Yahoo news, Google news) may adversely impact user experience. Hence, it is important to identify and demote or block them on homepages. In this paper, we present a machine-learning model to detect clickbaits. We use a variety of features and show that the degree of informality of a webpage (as measured by different metrics) is a strong indicator of it being a clickbait. We conduct extensive experiments to evaluate our approach and analyze properties of clickbait and non-clickbait articles. Our model achieves high performance (74.9% F-1 score) in predicting clickbaits.

How about trying to find any use of the famous Monty Hall problem in a stock index context? First of all, some of you may be confused because neither "Monty Hall problem" nor "Let's make a deal" are familiar to you so I will refresh you what these names are concerned to. Monty Hall was a TV presenter for "Let's make a deal", a famous American show in the sixties. Suppose you're on this game show and you're given the choice of three doors: behind one door there is a prize; behind the others, there is nothing. You pick a door, say number 1, and the host, who knows what's behind the doors, opens another door, say number 3, which results to be empty.

Impressive dinners and high bar tabs could soon be things of the past with a new futuristic way of dating. Lucy Kelly, who used a droid to keep her place in line at an Apple store last year, wants to now use the telepresence robot to fill in the voids of intimacy that are often linked to online dating. Users could sit at home while the robot joins their date miles away at a coffee shop-- adding a more personal element to an inexpensive first encounter. Lucy Kelly, who used a droid to keep her place in line at an Apple store last year, wants to use a telepresence robot to fill in the voids of intimacy that are often linked to online dating.The robot can be controlled from anywhere there is internet, so users can have a conversation with a potential suitor miles away The telepresence robot, created by Double Robotics, has lateral stability control that lets the device move with ease across different surfaces and around obstacles. Its power drive enables it to go up to 80 percent faster than normal driving speed and is controlled by holding down the shift key on the keyboard.

John Williams is well-known for his melodies in his music for films and concerts. In today's episode of Film Cue Review, we find out that his gift to support the film goes much deeper than we had anticipated. We take a look at his harmonic and orchestrational choices on the cue "Hide & Seek" from Steven Spielberg's 2001 film, Artificial Intelligence.

Thanks to Siri, we have a date for Apple's annual Worldwide Developers Conference. The tech giant will host its developers event June 13-17 in San Francisco. Although Apple did not release a formal statement detailing the timing of WWDC, users could uncover details by asking digital voice assistant Siri "when is WWDC?" The event usually takes place at the Moscone Center in San Francisco. As with previous years, Apple will likely reveal the first details on iOS 10, the latest version of its mobile operating system.

Reinforcement Learning, a key Google DeepMind algorithm, could overhaul news recommendation engines and greatly improve users stickiness. After beating a Go Grand Master, the algorithm could become the engine of choice for true personalization. My interest for DeepMind goes back to its acquisition by Google, in January 2014, for about half a billion dollars. Later in California, I had conversations with Artificial Intelligence and deep learning experts; they said Google had in fact captured about half of the world's best A.I. minds, snatching several years of Stanford A.I. classes, and paying top dollar for talent. Acquiring London startup Deep Mind was a key move in a strategy aimed at cornering the A.I. field.

Two images released last week from upcoming films highlight one glaring Hollywood problem. The first was the visage of Tilda Swinton in the debut trailer for Marvel's "Doctor Strange," in which the actress -- shorn and wearing the white robes of a Tibetan monk -- plays the Ancient One. In the "Doctor Strange" comics, the Ancient One is like Dumbledore meets Yoda: When the shattered former surgeon Stephen Strange makes his way deep into the Himalayas looking for enlightenment and redemption, he becomes a student of the mystically adept, absolutely Asian Ancient One. Marvel's cinematic universe just got a whole lot spookier, and a bit more magical. The world premiere of his new Marvel movie "Doctor Strange" (directed by longtime horror director Scott Derrickson) is loaded with lots of mystical charm with a serious twist.

Welcome to California Inc., the weekly newsletter of the L.A. Times Business Section. Expect financial markets to face headwinds today after the Federal Reserve reported Friday that U.S. industrial production fell more than expected in March. This is the latest sign that economic growth slowed significantly in the first quarter. On the plus side, though, many economists still forecast a rebound in growth as the year plods ahead. Tax deadline: Monday is the deadline for most Americans to submit their tax returns.