In recent years, between lockdowns, curfews, supply chain disruptions, and energy crunches, retailers must have felt like dinosaurs trying to dodge a rain of asteroids and avoid extinction. But unlike those giant prehistoric reptiles, the retail industry could count on a full array of technological innovations to better meet the challenges of these difficult times. One of the most impactful tools in this arsenal has certainly turned out to be artificial intelligence, including its powerful sub-branch known as machine learning (ML). Let's briefly frame the nature of this technology and explore the key use cases of machine learning in retail. Machine learning in retail relies on self-improving computer algorithms created to process data, spot recurring patterns and anomalies among variables, and autonomously learn how such relations affect or determine the industry's trends, phenomena, and business scenarios.

Context has been an important topic in recommender systems over the past two decades. A standard representational approach to context assumes that contextual variables and their structures are known in an application. Most of the prior CARS papers following representational approach manually selected and considered only a few crucial contextual variables in an application, such as time, location, and company of a person. This prior work demonstrated significant recommendation performance improvements when various CARS-based methods have been deployed in numerous applications. However, some recommender systems applications deal with a much bigger and broader types of contexts, and manually identifying and capturing a few contextual variables is not sufficient in such cases. In this paper, we study such ``context-rich'' applications dealing with a large variety of different types of contexts. We demonstrate that supporting only a few most important contextual variables, although useful, is not sufficient. In our study, we focus on the application that recommends various banking products to commercial customers within the context of dialogues initiated by customer service representatives. In this application, we managed to identify over two hundred types of contextual variables. Sorting those variables by their importance forms the Long Tail of Context (LTC). In this paper, we empirically demonstrate that LTC matters and using all these contextual variables from the Long Tail leads to significant improvements in recommendation performance.

Artificial intelligence, also known as AI, involves using computers to carry out tasks that would normally require human intelligence, such as understanding natural language and recognizing patterns. AI works by using a number of different methods to mimic human intelligence, including machine learning, natural language processing and artificial neural networks. An artificial intelligence contact center is a customer service center that uses AI technology to help employees respond to customer inquiries. It can be used to automate tasks such as routing calls, providing information about products and services, and handling complaints. What are chatbots and virtual assistants?

Conversational AI systems can engage in unsafe behaviour when handling users' medical queries that can have severe consequences and could even lead to deaths. Systems therefore need to be capable of both recognising the seriousness of medical inputs and producing responses with appropriate levels of risk. We create a corpus of human written English language medical queries and the responses of different types of systems. We label these with both crowdsourced and expert annotations. While individual crowdworkers may be unreliable at grading the seriousness of the prompts, their aggregated labels tend to agree with professional opinion to a greater extent on identifying the medical queries and recognising the risk types posed by the responses. Results of classification experiments suggest that, while these tasks can be automated, caution should be exercised, as errors can potentially be very serious.

Artificial intelligence (AI) has evolved into a personal digital assistant rather than a physical machine like the Terminator, says Samer Shoueiry of the creative communications group Publicis Communications. And it is developing at a speed that is "incredible", adds the regional executive head of digital and social marketing for the company. "We see AI today in everything we do as the personal digital assistant – someone returning your web searches, filtering your spam emails, the technology assisting everything within your car," he says. AI can be seen in every aspect of technology that links to digitisation, adds Mr Shoueiry, who spoke at the recent Dubai Lynx advertising festival. Research company Forrester claims that cognitive technologies such as AI and machine learning will replace 16 per cent of US jobs by 2025, in a report it published last year entitled Sharing Your Cubicle With Robots.

Days after hosting a major hardware launch, Amazon is apparently having a sale on its older Echo devices. Among the deals, we noticed that both the Echo Show 5 and the larger Echo Show 8 have hit new record lows. The Echo Show 5, which went on sale last year for $85, is now down to $35, a 59 percent discount. The 8-inch model, meanwhile, is down to $70 after having debuted at $100. Both devices, but especially the Echo Show 8, were designed to be used as a possible alarm clock, with a sunrise alarm feature that gently wakes you up by slowly brightening the display.

Executives across industries are turning to automation to deliver on cost optimization and enhanced productivity objectives, Saikat Ray, VP analyst at Gartner, told CIO Dive in August. The robotic process automation software market will reach $2.9 billion by the end of 2022, up 19.5% from 2021, according to Gartner. In recent years, large UiPath bank customers have been using automation tools to facilitate initiatives that include data extraction and data transfer efforts to support the merger of BB&T and SunTrust; reduction of manual work for Wells Fargo contact center agents through digital personal assistants; and the delegation of some structured, rule-based repetitive tasks to bots at JPMorgan Chase. From JPMorgan Chase's perspective, one of the next steps on its automation journey will include using bots to tackle more sophisticated tasks, including delving into unstructured processes and unstructured data, and using machine learning to facilitate these efforts, said Shefali Shah, managing director of global digital transformation and integrated intelligent automation at JPMorgan Chase. Diana Caplinger, executive vice president and head of enterprise enablement and intelligent automation at Truist, said the company is deploying automation in support of "integrated relationship management," an effort to use data across the organization to deliver more personalized service to clients.

Conversational AI has advanced dramatically since the early days of chatbots with limited capabilities. As time goes on, more industries are realizing the capabilities and benefits these advancements bring. Digital assistants like Alexa and Siri have consumers wondering why the same capabilities can't be used at work. While there are enterprise versions of Alexa and Cortana, conversational AI is still not at a point where a user can ask any question and receive a coherent answer. Like most other types of AI, the best use cases are narrow as opposed to broad.

We showcase a novel solution to a recommendation system problem where we face a perpetual soft item cold start issue. Our system aims to recommend demanded products to prospective sellers for listing in Amazon stores. These products always have only few interactions thereby giving rise to a perpetual soft item cold start situation. Modern collaborative filtering methods solve cold start using content attributes and exploit the existing implicit signals from warm start items. This approach fails in our use-case since our entire item set faces cold start issue always. Our Product Graph has over 500 Million nodes and over 5 Billion edges which makes training and inference using modern graph algorithms very compute intensive. To overcome these challenges we propose a system which reduces the dataset size and employs an improved modelling technique to reduce storage and compute without loss in performance. Particularly, we reduce our graph size using a filtering technique and then exploit this reduced product graph using Weighted Averaging of Messages over Layers (WAML) algorithm. WAML simplifies training on large graphs and improves over previous methods by reducing compute time to 1/7 of LightGCN and 1/26 of Graph Attention Network (GAT) and increasing recall$@100$ by 66% over LightGCN and 2.3x over GAT.

Online stores and service providers rely heavily on recommendation softwares to guide users through the vast amount of available products. Consequently, the field of recommender systems has attracted increased attention from the industry and academia alike, but despite this joint effort, the field still faces several challenges. For instance, most existing work models the recommendation problem as a matrix completion problem to predict the user preference for an item. This abstraction prevents the system from utilizing the rich information from the ordered sequence of user actions logged in online sessions. To address this limitation, researchers have recently developed a promising new breed of algorithms called sequence-aware recommender systems to predict the user's next action by utilizing the time series composed of the sequence of actions in an ongoing user session. This paper proposes a novel sequence-aware recommendation approach based on a complex network generated by the hidden metric space model, which combines node similarity and popularity to generate links. We build a network model from data and then use it to predict the user's subsequent actions. The network model provides an additional source of information that improves the accuracy of the recommendations. The proposed method is implemented and tested experimentally on a large dataset. The results prove that the proposed approach performs better than state-of-the-art recommendation methods.