You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine.

Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. In 2013, after a decade in Silicon Valley, neuroscientist/designer duo Anand Chandrasekaran and Ashwini Asokan started Mad Street Den with the aim of taking computer vision technology it beyond the realm of scientific research. Today, through its Vue.ai business unit, the company helps retailers such as Diesel, Off-White and Tata CLiQ grow their businesses by reducing operational costs and increasing revenue through automation, and by creating personalized customer experiences. "Think of Vue.ai as a vertically integrated stack for the retail industry," said Asokan, who in addition to having co-founded of Mad Street Den serves as CEO of Vue.ai. "Today, a retailer has to shop across tens of vendors to avail a CDP, a recommendation system, a search engine, a styling and cross-sell solution, a marketing automation engine, A/B testing software, workflow automation -- the list is absolutely endless."

Automated Machine Learning (AutoML) is an upcoming field in machine learning (ML) that searches the candidate model space for a given task, dataset and an evaluation metric and returns the best performing model on the supplied dataset as per the given metric. AutoML not only reduces the manpower and expertise needed to develop ML models but also decreases the time-to-market for ML models substantially. We have designed an enterprise-scale AutoML framework called WALTS to meet the rising demand of employing ML in retail or any other business of interest, and thus help democratize ML within our organization. In this blog, we elaborate on how we explore models from a pool of candidates and underline how it has helped us with a business use-case. To give an overview of the AutoML process, its current landscape, and showcase the benefits of WALTS, we will be covering: · What is AutoML?

In model selection problems for machine learning, the desire for a well-performing model with meaningful structure is typically expressed through a regularized optimization problem. In many scenarios, however, the meaningful structure is specified in some discrete space, leading to difficult nonconvex optimization problems. In this paper, we connect the model selection problem with structure-promoting regularizers to submodular function minimization with continuous and discrete arguments. In particular, we leverage the theory of submodular functions to identify a class of these problems that can be solved exactly and efficiently with an agnostic combination of discrete and continuous optimization routines. We show how simple continuous or discrete constraints can also be handled for certain problem classes and extend these ideas to a robust optimization framework. We also show how some problems outside of this class can be embedded within the class, further extending the class of problems our framework can accommodate. Finally, we numerically validate our theoretical results with several proof-of-concept examples with synthetic and real-world data, comparing against state-of-the-art algorithms.

In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that solve common business problems. These features remove the heavy lifting from each step of the ML process, making it easier to develop high-quality models and reducing time to deployment. This post is the fifth in a series on using JumpStart for specific ML tasks. In the first post, we showed how you can run image classification use cases on JumpStart.

In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart, a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that solve common business problems. These features remove the heavy lifting from each step of the ML process, making it easier to develop high-quality models and reducing time to deployment. This post is the fourth in a series on using JumpStart for specific ML tasks. In the first post, we showed how to run image classification use cases on JumpStart.

The field of computer vision in retail is getting bigger. More and more retail and e-commerce enterprises are using computer vision solutions to meet customer needs better and keep track of inventory. Clearly, there are no limits to how AI and computer vision can be used in retail. Let's talk in-depth about how artificial intelligence based computer vision is used in retail and what it can do. Retail AI and vision can enhance people's shopping experiences and retailers' overall profits in various ways, including image optimization, analysis of consumer behavior, management of shelf space, and monitoring of in-store health.

In a bid to increase sales and reduce food waste, more and more of Japan's convenience stores and supermarkets are letting artificial intelligence analyze data such as past sales figures to decide what discounts to apply to perishable products. At about 3 p.m., in a branch of the Lawson convenience store chain in Tokyo's Shinjuku Ward, an in-store computer decides which food products should be discounted and by how much. A display reads "assorted sandwiches, one, ¥20 ($0.13) discount," another, "crispy ham and lettuce sandwiches, three, ¥30 discount." This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. In the ecommerce world, digital images are everything. A new platform is using artificial intelligence (AI) to give brands the ability to evaluate visual content "through the eyes" of their target audiences in real time. Vizit, a provider of image analytics software for global brands and retailers, uses the organic interactions millions of consumers have with online commercial imagery to generate new AI-powered models of their visual preferences, called Vizit Audience Lenses. The goal is to ensure the images a brand uses for representing its products online are effective at capturing the desired audience's attention and triggering sales conversions.

In the ecommerce world, digital images are everything. A new platform is using artificial intelligence (AI) to give brands the ability to evaluate visual content "through the eyes" of their target audiences in real time. Vizit, a provider of image analytics software for global brands and retailers, uses the organic interactions millions of consumers have with online commercial imagery to generate new AI-powered models of their visual preferences, called Vizit Audience Lenses. The goal is to ensure the images a brand uses for representing its products online are effective at capturing the desired audience's attention and triggering sales conversions. Vizit is the latest entrant in the crowded digital shelf space, which refers to wherever products are made visible and available for online purchase.