Summary: There are five basic styles of recommenders differentiated mostly by their core algorithms. You need to understand what's going on inside the box in order to know if you're truly optimizing this critical tool. In our first article, "Understanding and Selecting Recommenders" we talked about the broader business considerations and issues for recommenders as a group. In this article we'll cover the five basic types of recommenders and their strengths and weaknesses. Given that Recommenders add 10% to 25% of incremental income to your ecommerce business you need to know exactly how these are working.

It's the new year, and TV shows are starting to finally filter back to the airwaves, including Gotham last night. While fansites have been reporting this for a while now, it was the first time that I realized that the show was actually bringing back Cameron Monaghan's Jerome, the show's "proto-Joker" that was (spoilers) killed off anti-climactically in a frankly fantastic arc that seemed oddly short, given the caliber of the performance and the importance of Joker to the entire Batman universe. But now Jerome is back, at least for another few-episode arc. Gotham has crafted a new plotline where despite Jerome's death, a cult is trying to resurrect his corpse Frankenstein-style so he can come back and wreak more havoc in Gotham. I do not believe the timing of this is coincidental.

In the wake of the 2016 election, marred by questions of the influence of fake news, the biggest presences on the web have been tasked with figuring out ways to stop the spread of false information. While those companies are at work on their own solutions, Stanford student Karan Singhal believes he has a better answer. The 19-year-old computer science major is taking as much of the human element out of the process of fake news detection as possible with Fake News Detector AI --a website and Google Chrome extension designed to sniff out fake news sites. To accomplish the task, Singhal is employing neural networks--a sort of artificial brain that can process a number of factors at a time, weighing each element and producing a verdict on the validity of a particular website. Instead of tasking people with fact checking individual claims or trying sort between bias and outright falsehoods, the Fake News Detector AI goes under the hood of a website in question and examines its parts, which can be more revealing than the text on the screen. Singhal told IBTimes his algorithmic method sorts over site layout, popularity, writing style, the frequency of telling keywords like "liberal" and "conservative," among other aspects of a given site.

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The Beijing-based company said Qi Lu was named group president and chief operating officer in charge of products, technology and sales. Qi, who has a Ph.D. from Carnegie Mellon University, previously was Microsoft Corp.'s global executive vice president.

The following post was first published in an earlier edition of Marketing Insider: Cross-Channel. Creating original branded content solves many problems for marketers, but also presents challenges -- among them distribution and realizing ROI from what can be a costly investment. Time Inc., CBS and Telepictures are among hundreds of publishers working with IRIS.TV, which recently introduced a product to manage the distribution of branded content. Its video personalization solution uses artificial intelligence and machine learning technology so publishers can automate the programming of their video libraries for the individual based on that person's preferences and behavior. We spoke with Rohan Castelino, director of business development and marketing with IRIS.TV, about how this works.

Now that you have an awesome, king-of-the-universe bot developed, packaged, and ready to roll, it's time to put it in front of consumers. It's time to make it discoverable. Making your bot discoverable is no different than making your mobile app or your website discoverable. It's a process that needs to be approached methodically. Let's take an imaginary "Sports News Delivery Bot" as an example and see how three key prerequisites may help you reach your potential consumers.

It's 2025 (give or take), and the long-awaited Big One has hit the San Francisco Bay Area. In the frenetic aftermath, teams of specialized rescue workers begin tearing through piles of wreckage--searching for signs of life, administering care, and calling for backup. As Stanford University's leading AI scientist Fei-Fei Li imagines it, they're robots with the smarts to "see" through their immediate surroundings and respond to humans in need, saving the maximum number of lives they can. The enabling technology behind this scenario is one Li has thought about and researched deeply--and it's not too far off, she argues, if computers can master what is arguably humankind's most complicated cognitive ability: vision. Current research, led by Li and the Stanford Artificial Intelligence Laboratory she directs, has already gotten us partially there, thanks to a database of more than 15 million digital images built in 2009.

Today, even though similar approaches are in use, they are usually just one part of complex recommendation approaches that can include large collections of algorithms and data sources. The data set was again provided year that the summer school on Recommender Systems by Moviepilot and was co-organized by TU Berlin. By 2007, the Netflix Prize had The second track focused on recommendation of scientific attracted thousands of participating teams, and the papers. The challenge attracted 30 participating Netflix Prize concluded. At the by Simon Fraser University and Yelp who also 2010 ACM RecSys conference, the seed for what provided the data. CAMRa attracted a moderate the 2014 challenge did not focus on classical recommendation, number of participants, but contributed to establishing but rather on prediction of user engagement, the RecSys Challenge series.

Can the language used in movie reviews hold the tea leaves revealing the winners of the Academy Awards? That's the hypothesis of Luminoso Technologies, an artificial-intelligence startup that specializes in natural-language processing, which has already declared the likely best-picture winner of the 2017 Academy Awards before the nominations are even out: Pablo Larraín's biopic "Jackie," starring Natalie Portman as Jacqueline Bouvier Kennedy. Here's the methodology: The company analyzed user movie reviews for 2013-15 in IMDb, focusing on the 50 most popular movies of each year, to see if there was a correlation behind the concepts that appeared in their language and the eventual Oscar nominees that year. Luminoso's software found certain specific concepts -- such as "cinematography," "masterpiece," "stunning," "visuals" and "experience" -- were highly correlated with films that received nominations. Concepts like "narrative" had less correlation with Oscar nods, and a few (like "CGI" and "horror") had negative correlation.