Screenshot shows a portion of the what should I watch experience
The new What Should I Watch (WSIW) experience, released in mid-September, combines Alexa AI and Fire TV recommendations to turn Alexa into an entertainment expert who provides relevant suggestions with a conversational customer experience.

The science behind the new “Alexa, what should I watch?” Fire TV experience

The phrase launches a feature built to help customers navigate an increasingly complex and diverse world of content.

"What should I watch?"

In an entertainment universe filled with a rapidly expanding catalog of shows across myriad channels and apps, this might be one of the most common questions to pop up in many households. And if you are among those who have trouble keeping up with all the latest shows and pinpointing which ones are worth your time, you are not alone.

In fact, more than half of respondents in a recent survey from the consulting firm Deloitte found it difficult to access content across multiple services, and 49% were frustrated if a service failed to provide them with good recommendations. Viewers find themselves surfing … and surfing. It takes the average smart TV owner 12 minutes to land on a show, according to a 2020 survey by Tivo — and for some viewers that can take up to half an hour.

"It's kind of shocking how much time customers have to spend on finding content instead of just sitting down on the couch and jumping into a TV show or a movie that they really enjoy," said Cosmin Laslau, a technical program manager who works on spoken language understanding as part of the Amazon Alexa Entertainment team. "We wanted to leverage new technology to help solve that problem for customers."

Image shows the new Fire TV Cube, left, the Fire TV Omni QLED Series, middle, and the Alexa Voice Remote Pro, right
The What Should I Watch experience works with many Fire TV devices, including the new Fire TV Cube, left, the Fire TV Omni QLED Series, middle, and the Alexa Voice Remote Pro announced at the 2022 Devices and Services event.

The team did that by launching What Should I Watch (WSIW). The new experience, released in mid-September, combines Alexa AI and Fire TV recommendations to turn Alexa into an entertainment expert who provides relevant suggestions with a conversational customer experience. The experience also works with the new Fire TV Cube, the Fire TV Omni QLED Series, and the Alexa Voice Remote Pro announced at the 2022 Devices and Services event.

“We built WSIW to rapidly experiment with new Alexa technologies and push the envelope on discovery experiences to address the core customer need to find something interesting to watch,” explained Parthasarathi Dutta Sharma, a product manager who helped bring WSIW to customers.

WSIW displays personalized recommendations when customers ask, “Alexa, what should I watch?” or a variant of that phrase. Customers can then customize the recommendations using voice prompts (for example, “just the ones that are free to me”) or by using their remote to select filters on the screen, watch trailers, view additional information (eg genre, ratings), and initiate playback.

Related content
Rohit Prasad on the pathway to generalizable intelligence and what excites him most about his re:MARS keynote.

The experience combines innovation for both Fire TV, with its extensive catalog, search and recommendation features, and the conversational AI that drives Alexa.

"We wanted to layer on these new innovations that have been developed around Alexa Conversations specifically," Laslau said. "We've given customers a broad range of natural ways to interact with Alexa, without being limited to a single utterance."

Since previewing WSIW last fall and beginning beta testing with customers, teams have worked to refine the customer experience.

“We used beta testing to closely observe how customers interacted with WSIW and to validate our core hypotheses on what works for customers,” explained Dutta Sharma. “A prime hypothesis we validated was viewers naturally gravitate to using natural language, with variability in inputs, while interacting with Alexa.”

Related content
Dialogue simulator and conversations-first modeling architecture provide ability for customers to interact with Alexa in a natural and conversational manner.

For example, to customize recommendations, the team found that initially customers might say, “I am in the mood for something funny”. They would then follow that by asking, “Which of these are on Prime Video?” or simply stating, “free to me”. So, the team worked to ensure WSIW could support those types of interactions with Alexa. It proved to be a feature customers responded to enthusiastically.

The team also responded to early feedback by introducing more gradual introductions to autoplay trailers and swapped an intro video on how to use the WSIW feature with on-screen contextual hints.

“Another insight was that customers wanted to be able to view only the titles they were already entitled to — versus those for rent or purchase — so we added a permanent free-to-me filter. Customers routinely call that out as a highlight,” Dutta Sharma said.

Building AI for the entertainment space

The What Should I Watch experience builds upon existing Alexa natural language understanding and automatic speech recognition capabilities.

"But bringing natural conversation to the entertainment domain has its own set of unique challenges," Laslau explained. Maybe a show, like The Boys or The Expanse, is ambiguously named, or a movie starts to trend that wasn't in the catalog a week or two ago. Optimizing the feature required combining core advances in AI around natural, multi-turn conversations with a fast-changing catalog.

"We are making sure those natural conversations are intelligent enough to reflect the very latest of what's happening in entertainment," he said.

The team also worked to ensure a mix of personalization based on your preferences— those British detective series you always gravitate toward — and something new that you might not have seen otherwise.

They did this by customizing Fire TV's existing recommender technology, mixing personalization with popular titles and randomizing subsets of these lists so that viewers encounter fresh ideas each time they turn on the TV.

A flywheel effect on innovation

The deep-learning-based Alexa Conversations makes it far simpler to develop the thousands of potential dialogue turns that a “What Should I Watch?” utterance might generate.

Alexa Conversations comprises three models: entity recognition (identifying Tom Cruise as an actor, for example), action prediction (utilizing the “movie searching” API to find movies), and argument filling (indicating the movies to be those with Tom Cruise).

“Alexa Conversations is designed to reduce the burden on developers, generating variations of dialogue automatically. The team has added several new features recently,” said Jiun-Yu Kao, an applied scientist within the Alexa AI Natural Understanding organization.

The WSIW experience is the first to launch with enhanced understanding of screen context.
Jiun-Yu Kao

Those include conversational Q&A which allow customers to ask broad questions about the recommended titles, such as which movies won an Oscar; a context reset function that allows a user to "start over" with a blank slate; and visual context, which enhances Alexa’s ability to respond correctly when a viewer says something like, "play the one on the left,” referencing what’s on the screen instead of naming the movie title.

“The WSIW experience is the first to launch with enhanced understanding of screen context,” Kao said. “It is also the first to combine all above-listed features for improved customer experience.”

Alexa and Fire TV science, engineering, and product teams collaborated to build the different components of the new feature.

Related content
A behind-the-scenes look at the unique challenges the engineering teams faced, and how they used scientific research to drive fundamental innovation to overcome those challenges.

“What’s super cool is that we are tapping into so many different services in parts of Alexa and Fire TV,” said Carlos Mattoso, a Fire TV software development engineer. “We are using a lot of the domain knowledge and capabilities that Fire TV has built around the recommendation space, for instance. But where we do that, we’re also trying to raise the bar: How can we use the information we’re gleaning from usage of What Should I Watch back into the system so that we have this flywheel that continuously improves?”

Mattoso noted that work with the Alexa team enabled not just suggestions but new in-context commands for Fire TV playback and volume changes, for example, that weren’t previously available.

“For instance, when we were building the first beta, we did not really have a way of initiating playback of a title from within an Alexa skill for Fire TV,” he explained. “So, we worked together with the Alexa Video team to extend the existing capability and then add support for that feature so that we could use it on WSIW.”

Looking ahead

Teams continue to work on making What Should I Watch faster and smarter.

One possibility is for users to explicitly guide Alexa by saying something like, "I'm a big sci-fi fan," or "I don't like horror movies." This type of interaction represents an opportunity for Alexa to adapt to customer engagement preferences, with some preferring to guide the service directly, and others wanting to lean back and take in recommendations.

As collaboration on the experience continues, both Alexa and Fire TV are becoming more capable. That could have a broader effect, particularly for the Alexa skill development community.

“We’re really trying to raise the bar,” Mattoso said, “and the capabilities we develop may eventually benefit third-party skill developers. Those might include improved long-term memory, better context resetting, and better visual context understanding.”

Research areas

Related content

US, WA, Seattle
The Amazon Devices and Services organization designs, builds and markets Kindle e-readers, Fire Tablets, Fire TV Streaming Media Players and Echo devices. The Device Economics team is looking for an Economist to join our fast paced, start-up environment to help invent the future of product economics. We solve significant business problems in the devices and retail spaces by understanding customer behavior and developing business decision-making frameworks. You will build econometric and machine learning models for causal inference and prediction, using our world class data systems, and apply economic theory to solve business problems in a fast-moving environment. This involves analyzing Amazon Devices and Services customer behavior, and measuring and predicting the lifetime value of existing and future products. We build scalable systems to ensure that our models have broad applicability and large impact. You will work with Scientists, Economists, Product Managers, and Software Developers to provide meaningful feedback about stakeholder problems to inform business solutions and increase the velocity, quality, and scope behind our recommendations. Key job responsibilities Applies expertise in causal modeling to develop econometric/machine learning models to measure the economic value of devices and the business Reviews models and results for other scientists, mentors junior scientists Generates economic insights for the Devices and Services business and work with stakeholders to run the business for effectively Describes strategic importance of vision inside and outside of team. Identifies business opportunities, defines the problem and how to solve it. Engages with scientists, business leadership outside Devices and Services to understand interplay between different business units We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Seattle, WA, USA
US, WA, Seattle
Amazon Advertising's Publisher Technologies team is looking for an experienced Applied Scientist with proven research experience in control theory, online machine learning, and/or mechanism design to drive innovative algorithms for ad-delivery at scale. Your work will directly shape pacing, yield optimization, and ad-selection for Amazon's publishers and impact experiences for hundreds of millions of users and devices. About the team Amazon Advertising operates at the intersection of eCommerce, streaming, and advertising, offering a rich array of digital advertising solutions with the goal of helping our customers find and discover anything they want to buy. We help advertisers reach customers across Amazon's owned and operated sites (publishers) across the web and on millions of devices such as Amazon.com, Prime Video, FreeVee, Kindles, Fire tablets, Fire TV, Alexa, Mobile, Twitch, and more. Within Ads, Publisher Technologies is building the next generation of ad-serving products to allow our publishers to monetize their on-demand, streaming, and static content across Amazon’s ad network in a few clicks. Publishers interact directly with our technology, through programmatic APIs to optimize billions of impression opportunities per day. About the role Publisher Technologies is looking to build out our Publisher Ad Server Science + Simulation and Experimentation team to drive innovation across ad-server delivery algorithms for budget pacing, ad-selection, and yield optimization. We seek to ensure the highest quality experiences for Amazon's customers by matching them with most relevant ads while ensuring optimal yield for publishers. As a Senior Applied Scientist, you will research, invent, and apply cutting edge designs and methodologies in control theory, online optimization, and machine learning to improve publisher yield and customer experience. You will work closely with our engineering and product team to design and implement algorithms in production. In addition, you will contribute to the end state vision of AI enhanced ad-delivery. You will be a foundational member of the team that builds a world-class, green-field ad-delivery service for Amazon's video, audio, and display advertising. To be successful in this role, you must be customer obsessed, have a deep technical background in both online algorithms and distributed systems, comfort dealing with ambiguity, an eye for detail, and a passion to identify and solve for practical considerations that occur when complex control-loops have to operate autonomously and reliably to make millisecond level decisions at scale. You are a technical leader with track record of building control theoretic and/or machine learning models in production to drive business KPIs such as budget delivery. If you are interested working on challenging and practical problems that impact hundreds of millions of users and devices and span cutting edge areas of optimization and AI while having fun on a rapidly expanding team, come join us! Key job responsibilities * Developing new statistical, causal, machine learning, and simulation techniques and develop solution prototypes to drive innovation * Developing an understanding of key business metrics / KPIs and providing clear, compelling analysis that shapes the direction of our business * Working with technical and non-technical customers to design experiments, simulations, and communicate results * Collaborating with our dedicated software team to create production implementations for large-scale data analysis * Staying up-to-date with and contributing to the state-of-the-art research and methodologies in the area of advertising algorithms * Presenting research results to our internal research community * Leading training and informational sessions on our science and capabilities * Your contributions will be seen and recognized broadly within Amazon, contributing to the Amazon research corpus and patent portfolio. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Seattle
The Alexa Economics team is looking for a Senior Economics Manager who is able to provide structure around complex business problems, hone those complex problems into specific, scientific questions, and test those questions to generate insights. The candidate will work with various product, analytics, science, and engineering teams to develop models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into data products at scale. They will lead teams of researchers to produce robust, objective research results and insights which can be communicated to a broad audience inside and outside of Alexa. Key job responsibilities Ideal candidates will work closely with business partners to develop science that solves the most important business challenges. They will work well in a team setting with individuals from diverse disciplines and backgrounds. They will serve as an ambassador for science for business teams, so that leaders are equipped with the right data and mental model to make important business decisions. Ideal candidates will own the development of scientific models and manage the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will be customer centric – clearly communicating scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. A day in the life - Review new technical approaches to understand Engagement and associated benefits to Alexa. - Partner with Engineering and Product teams to inject econometric insights and models into customer-facing products. - Help business teams understand the key causal inputs that drive business outcome objectives. About the team The Alexa Engagement and Economics and Team uses data, analytics, economics, statistics, and machine learning to measure, report, and track business outputs and growth. We are a team that is obsessed with understanding customer behaviors, and leveraging all aspects from customers behaviors with Alexa and Amazon to develop and deliver solutions that can drive Alexa growth and long-term business success. We use causal inference to identify business optimization and product opportunities. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Seattle, WA, USA
US, WA, Bellevue
We are seeking a passionate, talented, and inventive individual to join the Applied AI team and help build industry-leading technologies that customers will love. This team offers a unique opportunity to make a significant impact on the customer experience and contribute to the design, architecture, and implementation of a cutting-edge product. The mission of the Applied AI team is to enable organizations within Worldwide Amazon.com Stores to accelerate the adoption of AI technologies across various parts of our business. We are looking for an Applied Scientist to join our Applied AI team to work on LLM-based solutions. Key job responsibilities You will be responsible for developing and maintaining the systems and tools that enable us to accelerate knowledge operations and work in the intersection of Science and Engineering. You will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. A day in the life We are seeking an experienced Scientist who combines superb technical, research, analytical and leadership capabilities with a demonstrated ability to get the right things done quickly and effectively. This person must be comfortable working with a team of top-notch developers and collaborating with our research teams. We’re looking for someone who innovates, and loves solving hard problems. You will be expected to have an established background in building highly scalable systems and system design, excellent project management skills, great communication skills, and a motivation to achieve results in a fast-paced environment. You should be somebody who enjoys working on complex problems, is customer-centric, and feels strongly about building good software as well as making that software achieve its operational goals. About the team On our team you will push the boundaries of ML and Generative AI techniques to scale the inputs for hundreds of billions of dollars of annual revenue for our eCommerce business. If you have a passion for AI technologies, a drive to innovate and a desire to make a meaningful impact, we invite you to become a valued member of our team. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
US, WA, Bellevue
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Bellevue, WA, USA | Seattle, WA, USA
US, WA, Seattle
The ASFS Team is hiring an Intern in Economics. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics and macroeconomics, as well as familiarity with Python, Matlab, or R is necessary. This is a full-time position at 40 hours per week, with compensation being awarded on an hourly basis. You will use internal and external data to estimate macroeconometric models to answer critical business questions, also you will have the opportunity to collaborate with economists and data scientists. Roughly 85% of interns from previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | New York City, NY, USA | Seattle, WA, USA
US, WA, Bellevue
As an Applied Scientist on our Learning and Development team, you will play a critical role in driving the design, development, and delivery of learning programs and initiatives aimed at enhancing leadership and associate development within the organization. You will leverage your expertise in learning science, data analysis, and statistical model design to create impactful learning journey roadmap that align with organizational goals and priorities. Key job responsibilities 1) Research and Analysis: Conduct research on learning and development trends, theories, and best practices related to leadership and associate development. Analyze data to identify learning needs, performance gaps, and opportunities for improvement within the organization. Use data-driven insights to inform the design and implementation of learning interventions. 2) Program Design and Development: Collaborate with cross-functional teams to develop comprehensive learning programs focused on leadership development and associate growth. Design learning experiences using evidence-based instructional strategies, adult learning principles, and innovative technologies. Create engaging and interactive learning materials, including e-learning modules, instructor-led workshops, and multimedia resources. 3) Evaluation and Continuous Improvement: Develop evaluation frameworks to assess the effectiveness and impact of learning programs on leadership development and associate performance. Collect and analyze feedback from participants and stakeholders to identify strengths, areas for improvement, and future learning needs. Iterate on learning interventions based on evaluation results and feedback to continuously improve program outcomes. 4) Thought Leadership and Collaboration: Serve as a subject matter expert on learning science, instructional design, and leadership development within the organization. Collaborate with stakeholders across the company to align learning initiatives with strategic priorities and business objectives. Share knowledge and best practices with colleagues to foster a culture of continuous learning and development. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Nashville, TN, USA
US, WA, Seattle
Amazon Web Services (AWS) is building a world-class marketing organization, and we are looking for an experienced Economist to join the central data and science organization for AWS Marketing. This candidate will develop innovative solutions to measure the return on marketing investments. They will work closely with business leaders, scientists, and engineers to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of innovative measurement solutions. They will interact with functional leaders owning events (e.g. re:Invent, summits, webinars), paid media (paid search, paid social, display), AWS-owned channels (email, website, console) as well as lead management organization to drive the development, fine-tuning and adoption of the consistent measurement framework across these diverse initiatives. We seek candidates with an entrepreneurial spirit who want to make a big impact on AWS growth. They will develop strong working relationships and thrive in a collaborative team environment. They will have the creativity, curiosity, and strong judgment to work on high-impact, high-visibility products to improve the experience of AWS leads and customers. Key job responsibilities - Apply your expertise in causal inference and ML to develop systems to measure B2B marketing impact - Develop and execute science products from concept, prototype to production incorporating feedback from customers, scientists and business leaders - Identify new opportunities for leveraging economic insights and models in the marketing space - Write technical white papers and business-facing documents to clearly explain complex technical concepts to audiences with diverse business/scientific backgrounds We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Austin, TX, USA | New York City, NY, USA | Seattle, WA, USA
US, GA, Atlanta
Looking for your next challenge? North America Sort Centers (NASC) are experiencing growth and looking for a skilled, highly motivated Data Scientist to join the NASC Engineering Data, Product and Simulation Team. The Sort Center network is the critical Middle-Mile solution in the Amazon Transportation Services (ATS) group, linking Fulfillment Centers to the Last Mile. The experience of our customers is dependent on our ability to efficiently execute volume flow through the middle-mile network. Key job responsibilities The Senior Data Scientist will design and implement solutions to address complex business questions using simulation. In this role, you will apply advanced analysis techniques and statistical concepts to draw insights from massive datasets, and create intuitive simulations and data visualizations. You can contribute to each layer of a data solution – you work closely with process design engineers, business intelligence engineers and technical product managers to obtain relevant datasets and create simulation models, and review key results with business leaders and stakeholders. Your work exhibits a balance between scientific validity and business practicality. On this team, you will have a large impact on the entire NASC organization, with lots of opportunity to learn and grow within the NASC Engineering team. This role will be the first dedicated simulation expert, so you will have an exceptional opportunity to define and drive vision for simulation best practices on our team. To be successful in this role, you must be able to turn ambiguous business questions into clearly defined problems, develop quantifiable metrics and deliver results that meet high standards of data quality, security, and privacy. About the team NASC Engineering’s Product and Analytics Team’s sole objective is to develop tools for under the roof simulation and optimization, supporting the needs of our internal and external stakeholders (i.e Process Design Engineering, NASC Engineering, ACES, Finance, Safety and Operations). We develop data science tools to evaluate what-if design and operations scenarios for new and existing sort centers to understand their robustness, stability, scalability, and cost-effectiveness. We conceptualize new data science solutions, using optimization and machine learning platforms, to analyze new and existing process, identify and reduce non-value added steps, and increase overall performance and rate. We work by interfacing with various functional teams to test and pilot new hardware/software solutions. We are open to hiring candidates to work out of one of the following locations: Atlanta, GA, USA | Bellevue, WA, USA
US, WA, Bellevue
Amazon’s Middle Mile Planning & Optimization team is looking for an exceptional Sr. Applied Scientist to solve complex optimization problems that ensure we exceed customer delivery promise expectations and minimize overall operational cost while supporting Amazon’s rapid growth globally. We use cutting edge technologies in large-scale optimization, predictive analytics, and generative AI to optimize the flow of packages within our network to efficiently match network capacity with shipment demand. Our services already handle thousands of requests per second, make business decisions impacting billions of dollars a year, and improve the delivery experience for millions of online shoppers. That said, this remains a fast-growing business and our journey has just started. Our mission is to build the most efficient and optimal transportation solution on the planet, using our technology and engineering muscle as our biggest advantage. Key job responsibilities You will work closely with product managers, research scientists, business/operations leaders, and technical leadership to build capabilities that transform our transportation network. This includes analyzing big data, building end-to-end workflows, prototype optimization/simulation models, and launch production capabilities. You will have exposure to senior leadership as you communicate results and provide scientific guidance to the business. Your insights will be a key influencer of our product strategy and roadmap and your experimental research will inform our future investment areas. About the team You will join the Surface Research Science (SRS) team, which is the science partner of the Middle-Mile Planning & Optimization tech organization. SRS is working on a fascinating range of problems, including some of the hardest and largest optimization, simulation, and prediction problems in the industry. Examples are long-term and short-term demand forecasting, capacity planning, driver scheduling, vehicle routing, and equipment rebalancing problems. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA