In the very near future, Amazon's famed voice assistant, Alexa, may sound quite different from the dutiful (and impersonal) voice you've grown accustomed to since it rolled out in 2014. At least, that's what Rohit Prasad, Amazon's senior vice president and head scientist for Alexa, announced at Amazon's re:MARS conference, a global artificial intelligence (AI) event that Amazon founder and executive chair Jeff Bezos hosted over the summer. With just a one-minute audio sample, the technology could bring a loved one's voice bounding through an Echo device's speakers. Prasad used a short presentation to show the audience how the new speech-synthesizer technology could help us forge lasting memories of our deceased relatives. "Alexa, can grandma finish reading me The Wizard of Oz?" A young boy asked a cute Echo speaker with big Panda eyes.

In the past, brick-and-mortar retailers leveraged native marketing and advertisement channels to engage with consumers. They have promoted their products and services through TV commercials, and magazine and newspaper ads. Many of them have started using social media and digital advertisements. Although marketing approaches are beginning to modernize and expand to digital channels, businesses still depend on expensive marketing agencies and inefficient manual processes to measure campaign effectiveness and understand buyer behavior. The recent pandemic has forced many retailers to take their businesses online.

Session-based recommender systems (SBRSs) have shown superior performance over conventional methods. However, they show limited scalability on large-scale industrial datasets since most models learn one embedding per item. This leads to a large memory requirement (of storing one vector per item) and poor performance on sparse sessions with cold-start or unpopular items. Using one public and one large industrial dataset, we experimentally show that state-of-the-art SBRSs have low performance on sparse sessions with sparse items. We propose M2TRec, a Metadata-aware Multi-task Transformer model for session-based recommendations. Our proposed method learns a transformation function from item metadata to embeddings, and is thus, item-ID free (i.e., does not need to learn one embedding per item). It integrates item metadata to learn shared representations of diverse item attributes. During inference, new or unpopular items will be assigned identical representations for the attributes they share with items previously observed during training, and thus will have similar representations with those items, enabling recommendations of even cold-start and sparse items. Additionally, M2TRec is trained in a multi-task setting to predict the next item in the session along with its primary category and subcategories. Our multi-task strategy makes the model converge faster and significantly improves the overall performance. Experimental results show significant performance gains using our proposed approach on sparse items on the two datasets.

While Sam's Club at first glance may seem like a typical warehouse retail membership club, look beyond the pallets of packaged food and tabletops stacked with designer clothing, and you'll see an operation committed to using technology to improve the experience of customers – or members, as they are called – and its own operations. Case in point -- the company introduced the Scan and Go mobile phone app in 2016, allowing customers to avoid the check-out lines by using their mobile phones to scan barcodes themselves and then click a button to check out and pay. The service made shopping more convenient for the members who used it in 2016. But it really stood out as visionary when the pandemic hit in 2020. Scan and Go provided a contactless shopping experience at a time when the guidance on COVID was to "social distance" by staying 6 feet away from anyone else.

The dramatic growth of e-shopping will undoubtedly cause significant impacts on travel demand. As a result, transportation modeller's ability to model e-shopping demand is becoming increasingly important. This study developed models to predict households' weekly home delivery frequencies. We used both classical econometric and machine learning techniques to obtain the best model. It is found that socioeconomic factors such as having an online grocery membership, household members' average age, the percentage of male household members, the number of workers in the household and various land-use factors influence home delivery demand. This study also compared the interpretations and performances of the machine learning models and the classical econometric model. Agreement is found in the variable's effects identified through the machine learning and econometric models. However, with similar recall accuracy, the ordered probit model, a classical econometric model, can accurately predict the aggregate distribution of household delivery demand. In contrast, both machine learning models failed to match the observed distribution.

Retailers face an evolving landscape of fraud tactics each day. It’s why companies are increasingly turning to AI to try and catch threat patterns never seen before, and block attacks before they ever happen. While this approach lends itself to efficiency, it’s also one that relies on increasingly complex data profiles of consumers. In this…

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The global AI in retail market size is anticipated to reach USD 40.74 billion by 2030, expanding at a CAGR of 23.9% from 2022 to 2030, according to a new study by Grand View Research, Inc. The rising prominence of advanced technologies, such as chatbots and voice recognition programs, has furthered the growth potential. Moreover, the emerging online retail sales, increasing focus of retailers on improving customers' shopping experience, rising reliance on digital marketing, and growing investments in AI, accompanied by supportive government regulations, are the crucial factors contributing to the progress of the industry worldwide. Read 145 page full market research report for more Insights, "AI In Retail Market Size, Share & Trends Analysis Report By Component, By Technology (Chatbots, Natural Language Processing), By Sales Channel, By Application, By Region, And Segment Forecasts, 2022 - 2030", published by Grand View Research. AI algorithms play a pivotal role in assessing a considerable amount of data collated from consumers' online behavior.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Retailers and convenience stores have increasingly invested heavily in technology, especially mobile applications, that enable customers to do much of the shopping legwork from home. Are autonomous stores the next big thing in retail tech? Chris Hartman, senior director of fuels, forecourt, advertising and construction at Rutter's, thinks so.

In most applications of model-based Markov decision processes, the parameters for the unknown underlying model are often estimated from the empirical data. Due to noise, the policy learnedfrom the estimated model is often far from the optimal policy of the underlying model. When applied to the environment of the underlying model, the learned policy results in suboptimal performance, thus calling for solutions with better generalization performance. In this work we take a Bayesian perspective and regularize the objective function of the Markov decision process with prior information in order to obtain more robust policies. Two approaches are proposed, one based on $L^1$ regularization and the other on relative entropic regularization. We evaluate our proposed algorithms on synthetic simulations and on real-world search logs of a large scale online shopping store. Our results demonstrate the robustness of regularized MDP policies against the noise present in the models.