In many businesses, and particularly in finance, the behavior of a client might drastically change over time. It is consequently crucial for recommender systems used in such environments to be able to adapt to these changes. In this study, we propose a novel collaborative filtering algorithm that captures the temporal context of a user-item interaction through the users' and items' recent interaction histories to provide dynamic recommendations. The algorithm, designed with issues specific to the financial world in mind, uses a custom neural network architecture that tackles the non-stationarity of users' and items' behaviors. The performance and properties of the algorithm are monitored in a series of experiments on a G10 bond request for quotation proprietary database from BNP Paribas Corporate and Institutional Banking.

Recommender systems are everywhere, helping you find everything from movies to jobs, restaurants to hospitals, even romance. Using behavioral and demographic data, these systems make predictions about what users will be most interested in at a particular time, resulting in high-quality, ordered, personalized suggestions. Recommender systems are practically a necessity for keeping your site content current, useful, and interesting to your visitors.

Data can be used in a variety of ways to satisfy the needs of different business units, such as marketing, sales, or product. In this post, we focus on using data to create personalized recommendations to improve end-user engagement. Most ecommerce applications consume a huge amount of customer data that can be used to provide personalized recommendations; however, that data may not be cleaned or in the right format to provide those valuable insights. The goal of this post is to demonstrate how to use AWS Glue to extract, transform, and load your JSON data into a cleaned CSV format. We then show you how to run a recommendation engine powered by Amazon Personalize on your user interaction data to provide a tailored experience for your customers.

For most, the term Artificial Intelligence evokes thoughts of futuristic technologies, of C-3PO and Asimov's "I, Robot." We think of walking, sentient robots that look like us – or at least we imagine they appear to like us. What we don't generally think of is a glorified speakerbox, or a supercomputer tasked to create recipes. But that's where AI starts – with small steps and incremental advances in technology. These small steps gradually lead to giant leaps forward; think of a narrow canyon off in the distance: you're pretty sure you can jump the canyon, but first you need to get there.

This chapter aims at providing a framework for analysis on evolutionary trends in finance that have to do with technological progress and especially with artificial intelligence (AI) applications. The starting point can be identified with a survey on how they have modified the business areas involving banking and financial services and on what can be expected – in terms of future strategic shifts and behavioral changes – on both the supply and the demand sides. The next step revolves around a wider and deeper investigation on the role that virtual assistants have started to – and are likely to further – play in the areas under scrutiny: special attention is requested upon the provision of enhanced customer service support, including conversational AI and sound branding; implications encompass developments that are on the cards, based upon digitalization as a must – not just an option – as shown by the Covid-19 pandemic. Conclusions allow to emphasize the significance, advancing features and value of this conceptual paper, as it leads to sort out best practices and success stories that are worth disseminating and replicating to benefit not only individuals and enterprises having direct interest in them, but society as a whole.

Ever since the original Echo Show, smart displays have proven to be pretty useful around the house. They're great for quickly looking up information, recipe instructions, video calls and more. As you're actively crisscrossing the kitchen while cooking a meal, you have to keep backtracking to look at the screen for the next step. Or if you're on a video call with mom and you walk away to make a cup of coffee, you're forced to become a disembodied voice as she converses with a wall. The all-new Echo Show 10, however, helps solve that problem.

It was about 7:30 a.m. on a Friday when I asked the new Echo Show 10 to read me the news. I walked toward the coffee machine, bleary eyed, and poured my morning cup. When I finally looked up in the Echo Show's direction, it had turned to meet my gaze. As I walked back over, the screen panned again. In the morning light, I could see the device's pea-size, motion-detecting camera challenging me to a staring contest I'd never win. After experiencing the auto-tracking technology for the first time, I felt a mix of awe and dread.

In the hours after she shared this makeup experiment, it was shown to hundreds of thousands of people on their "For You" pages, the lifeblood of TikTok. It wasn't obvious to her why this particular post was suddenly so visible, except that TikTok's recommendation algorithms had made it so. Since TikTok launched in China in 2016, it has become one of the most engaging and fastest-growing social media platforms in the world. It's been downloaded more than 2.6 billion times globally and has 100 million users in the US. And the unique way it finds and serves up content is a big part of its appeal.

Artificial intelligence (AI) is obviously a developing power in the technology business. Artificial intelligence is becoming the dominant center point at conferences and indicating potential over a wide variety of industries, including retail and manufacturing. New products are being incorporated with virtual assistants, while chatbots are responding to client inquiries on everything from your online office provider's website to your web hosting service provider's support page. In the interim, organizations, for example, Google, Microsoft, and Salesforce are incorporating AI as an intelligence layer over their whole tech stack. Indeed, AI is definitely having its moment. For organizations, practical AI applications can demonstrate in a wide range of ways relying upon your organizational needs and the business intelligence (BI) insights gained from the data you gather.

Deep learning-based models are utilized to achieve state-of-the-art performance for recommendation systems. A key challenge for these models is to work with millions of categorical classes or tokens. The standard approach is to learn end-to-end, dense latent representations or embeddings for each token. The resulting embeddings require large amounts of memory that blow up with the number of tokens. Training and inference with these models create storage, and memory bandwidth bottlenecks leading to significant computing and energy consumption when deployed in practice. To this end, we present the problem of \textit{Memory Allocation} under budget for embeddings and propose a novel formulation of memory shared embedding, where memory is shared in proportion to the overlap in semantic information. Our formulation admits a practical and efficient randomized solution with Locality sensitive hashing based Memory Allocation (LMA). We demonstrate a significant reduction in the memory footprint while maintaining performance. In particular, our LMA embeddings achieve the same performance compared to standard embeddings with a 16$\times$ reduction in memory footprint. Moreover, LMA achieves an average improvement of over 0.003 AUC across different memory regimes than standard DLRM models on Criteo and Avazu datasets