In this paper, we propose a simple yet effective solution to build practical teacher recommender systems for online one-on-one classes. Our system consists of (1) a pseudo matching score module that provides reliable training labels; (2) a ranking model that scores every candidate teacher; (3) a novelty boosting module that gives additional opportunities to new teachers; and (4) a diversity metric that guardrails the recommended results to reduce the chance of collision. Offline experimental results show that our approach outperforms a wide range of baselines. Furthermore, we show that our approach is able to reduce the number of student-teacher matching attempts from 7.22 to 3.09 in a five-month observation on a third-party online education platform.

We propose a method to easily modify existing offline Recommender Systems to run online using Transfer Learning. Online Learning for Recommender Systems has two main advantages: quality and scale. Like many Machine Learning algorithms in production if not regularly retrained will suffer from Concept Drift. A policy that is updated frequently online can adapt to drift faster than a batch system. This is especially true for user-interaction systems like recommenders where the underlying distribution can shift drastically to follow user behaviour. As a platform grows rapidly like Grubhub, the cost of running batch training jobs becomes material. A shift from stateless batch learning offline to stateful incremental learning online can recover, for example, at Grubhub, up to a 45x cost savings and a +20% metrics increase. There are a few challenges to overcome with the transition to online stateful learning, namely convergence, non-stationary embeddings and off-policy evaluation, which we explore from our experiences running this system in production.

Join executive leaders at the Conversational AI & Intelligent AI Assistants Summit, presented by Five9. During the pandemic, enterprises turned to automation to scale up their operations while freeing customer service reps to handle increasingly challenging workloads. According to Canam Research, 78% of contact centers in the U.S. now intend to deploy AI in the next 3 years. And research from The Harris Poll indicates that 46% of customer interactions are already automated, with the number expected to reach 59% by 2023. The panel touched on how AI assistants can coexist with humans and help them to perform their jobs, while at the same time respecting existing customer service guardrails. "We're talking about a spectrum of technologies," Schebella said of automation broadly.

Join executive leaders at the Conversational AI & Intelligent AI Assistants Summit, presented by Five9. Capital One has a 99% success rate when it comes to understanding customer responses to an SMS fraud alert, Ken Dodelin, vice president of mobile, web, conversational AI and messaging products at Capital One, said in a discussion about how the bank harnesses the power of personalization and automation at VentureBeat's Transform 2021 virtual conference today. When Capital One notices an anomaly in a customer's transactions, it reaches out to the customer over SMS and asks the customer to verify the transaction details. If the customer doesn't recognize the transaction, then it is clear it was fraudulent and Capital One marks it accordingly. By adding a third-party natural language processing/understanding solution, the AI assistant Eno is able to understand written responses from the customers, such as "that was me shopping in Philadelphia," which is not easy for machines to understand, Dodelin said in a conversation with VentureBeat senior reporter Sage Lazzaro.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. You might consider trying a dating app. That's what 26-year-old Gaby Deimeke did after she moved to Austin, Texas, in 2019. After hearing about Bumble BFF at a music festival, Deimeke download the app and gave it a try.

Amazon's latest set of crowdfunded Echo devices aim for luxury over eccentricity. The retailer has unveiled three new trippy Echo Dot concepts from Belgian fashion designer Diane von Furstenberg (DVF) that you can pre-order today for $60 each. Well, as long as they hit their sales target. Like the trio of weird products Amazon unveiled in February (cuckoo clock anyone?) these dinky speakers are part of the Built It program that borrows from Kickstarter and Indiegogo. Basically, Amazon will only ship out this second round of gadgets if they generate enough consumer interest within 30 days.

Human beings are champions at spotting patterns, especially faces, in inanimate objects--think of the famous "face on Mars" in images taken by the Viking 1 orbiter in 1976, which is essentially a trick of light and shadow. And people are always spotting what they believe to be the face of Jesus in burnt toast and many other (so many) ordinary foodstuffs. There was even a (now defunct) Twitter account devoted to curating images of the "faces in things" phenomenon. This story originally appeared on Ars Technica, a trusted source for technology news, tech policy analysis, reviews, and more. Ars is owned by WIRED's parent company, Condé Nast.

Whether it's using QR codes to pull up menus at restaurants or ordering groceries for pickup or delivery online, people have gotten used to navigating the world at their own pace and in their own space during the pandemic. Now they're expecting the same types of experiences at theme parks, according to a newly released survey by Oracle and Merlin Entertainments, which operates various theme parks and attractions across the globe, including Legoland parks and Madame Tussauds. "COVID impacted how people interact," said Simon de Montfort Walker, General Manager of Oracle Food and Beverage. Oracle's point of sale software is used at concession and retail operations across Merlin theme parks and other major businesses like Marriott and Outback Steakhouse. Disney World holidays:Mickey's Very Merry Christmas Party being replaced with new Very Merriest event "We've all spent a lot less time waiting in lines," he said.

In recent years, recommendation systems have been widely applied in many domains. These systems are impotent in affecting users to choose the behavior that the system expects. Meanwhile, providing incentives has been proven to be a more proactive way to affect users' behaviors. Due to the budget limitation, the number of users who can be incentivized is restricted. In this light, we intend to utilize social influence existing among users to enhance the effect of incentivization. Through incentivizing influential users directly, their followers in the social network are possibly incentivized indirectly. However, in many real-world scenarios, the topological structure of the network is usually unknown, which makes identifying influential users difficult. To tackle the aforementioned challenges, in this paper, we propose a novel algorithm for exploring influential users in unknown networks, which can estimate the influential relationships among users based on their historical behaviors and without knowing the topology of the network. Meanwhile, we design an adaptive incentive allocation approach that determines incentive values based on users' preferences and their influence ability. We evaluate the performance of the proposed approaches by conducting experiments on both synthetic and real-world datasets. The experimental results demonstrate the effectiveness of the proposed approaches.

As it stands out today,Artificial intelligence elucidates simulation of human intelligence bymachines, particularly computer systems. AI programming focuses on three basiccognitive skills which are learning, reasoning and self-correction. Learning processes is theaspect of AI programming which focuses on acquiring data and creating rules forhow to turn the data into actionable information. These rules are calledalgorithms, and they provide the computing devices stepwise instructions on howto complete a specific task. Reasoning processes is theaspect of AI programming that focuses on choosing the right algorithm to reacha desired outcome. Typically, AI systems demonstrate at least some behaviours which are associated with human intelligence; thesebehaviours are planning,learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesserextent, social intelligence and creativity. The roots of computing dates back to the Logic Theoristprogram which was presented at the Dartmouth Summer scientific research onArtificial Intelligence (DSRPAI) hosted by John McCarthy and Marvin Minsky in1956.