RecSys 2022: “Recommenders are ubiquitous”

Adapting natural-language-processing techniques to recommendation systems and algorithmic fairness are two central topics at this year’s conference.

The ACM Conference on Recommender Systems (RecSys), the leading conference in the field of recommendation systems, takes place this week, and two Amazon scientists — Max Harper, a senior applied scientist, and Vanessa Murdock, a senior applied-science manager, both in the Alexa Shopping organization — are among the conference’s three general chairs, along with Jennifer Golbeck of the University of Maryland. Harper and Murdock spoke to Amazon Science about the conference program and what it indicates about the state of research on recommender systems.

Amazon Science: Can you tell us a little bit about RecSys?

Max Harper: RecSys has been around for a long time — since the ’90s — and it's a community that's interested in both algorithms and applications of machine learning techniques that model the behavior of users. In particular, RecSys focuses on domains where the definition of the best thing for the model to return depends on which person you ask. So it's personalized.

RecSys portrait.png
Senior applied scientist Max Harper (left) and senior applied-science manager Vanessa Murdock, both of the Alexa Shopping organization, are two of the three general chairs at this year's RecSys.

The classical applications include movies, music, and books, which are obviously taste-driven domains. But these days, it's expanded into tons of areas, including travel, fashion, and job finding.

In addition to algorithms and applications, I'd say about 20% of the field is interested in people, how people perceive recommendations, how to design user interfaces that work well and how to shape the user experience in a variety of ways.

There's also a whole host of machine learning issues that comes along with it, including how to measure performance, how to scale the algorithms, how to preserve users’ privacy. And finally, an increasingly important issue is the societal impacts of these algorithms.

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In 2017, when the journal IEEE Internet Computing was celebrating its 20th anniversary, its editorial board decided to identify the single paper from its publication history that had best withstood the “test of time”. The honor went to a 2003 paper called “Amazon.com Recommendations: Item-to-Item Collaborative Filtering”, by then Amazon researchers Greg Linden, Brent Smith, and Jeremy York.

Vanessa Murdock: I sit between the fields of search and recommendation, and they're somewhat different in that recommendations can be made even if the user isn't asking for them, whereas search is usually in response to a request.

Recommenders are ubiquitous — they’re in many of the apps and tools we use every day. For example, if you're looking for a coffee in Seattle, and you look at a map, the resolution of the map that you see on the first view will show you some points of interest, and then, if you zoom in, you'll see more. You can view those first points of interest as recommendations, but it's not what you usually think of as a recommender.

Your Instagram feed and Tik Tok are all recommendations. Your Twitter feed is a set of recommended tweets. It's central to our experience with the digital world in everything that we do.
Vanessa Murdock

All of this research on deciding what people would like to engage with has had significant influence on online commerce and ads and sponsored placements. Your Instagram feed and Tik Tok are all recommendations. Your Twitter feed is a set of recommended tweets. It's central to our experience with the digital world in everything that we do.

AS: In 2017, when IEEE Internet Computing celebrated its 20th anniversary, it gave its test-of-time award to Amazon’s 2003 paper on item-to-item collaborative filtering. How has the field evolved since that paper?

MH: The concept of collaborative filtering is still very, very relevant. These days, matrix factorization techniques are much more common; you use them to complete an item-customer matrix. But it's essentially the same class of techniques.

There's a paper at this year's RecSys, “Revisiting the performance of iALS on item recommendation benchmarks”, and it's part of the RecSys replicability track, which is kind of a unique thing at RecSys. This paper has to do with matrix factorization, which the field thinks of as an old-fashioned technique. And the point that authors make in this paper is that a well-tuned matrix factorization algorithm can hold its own against a whole range of more modern deep-learning algorithms.

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VM: The reproducibility track at RecSys is especially good because a lot of reported research is incremental gains over many years. In every paper, the numbers always go up, and the results are always significant, but the improvements don’t always add up over time. Having a reproducibility track really sets RecSys apart. It means that as we are making gains in some area, we can look back and say, “Is this really true?”

In my own work, I've found that when I've tried to reproduce work from other people, the results depend on the collection or the queries or the system parameters. And that's not what a scientific advance really should be. So I think that that's a very important track, and more conferences should add it.

Sequential recommendation

AS: What are some of the newer ideas in the field that you find most intriguing?

MH: If I were to pick the number one thing that seems to have taken over the conference, it would be the application of techniques from natural-language processing to the field of recommender systems. In particular, Transformers and large language models like BERT have been adapted to the context of recommendations in an interesting way.

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Essentially, these language models learn the semantics of sentences by modeling which words go with which other words, and you can take an analogous approach in the field of recommendations by looking at, not sentences of words, but sequences of items — for example, products at Amazon or movies at Netflix that users engage with. And by using similar training techniques to what they use in NLP, they can solve problems like next-item prediction: given that the user has looked at these three products most recently, what's the product that they're most likely to look at next?

Language models learn the semantics of sentences by modeling which words go with which other words, and you can take an analogous approach in the field of recommendations by looking at, not sentences of words, but sequences of items.
Max Harper

That concept is called sequential recommendation, and it is everywhere at RecSys this year.

AS: Does sequential recommendation use the same kind of masked training that language models do?

MH: Yeah, it does. You take a sequence of user behavior, and you hide one of the items that they actually interacted with and try to predict that that's part of the sequence.

AS: How is that approach adapted to the new setting?

MH: Two examples I can think of: One is that there's aren’t necessarily natural boundaries in a sequence of user interactions, so you might be tempted to look at the entire sequence of interactions in order to predict the next one. Researchers are looking at the degree to which recency is important in next-item recommendation.

Another one is that sentences are more predictable: if you're missing a word in a sentence, it's more likely that a human could guess what that word is. With a sequence of item clicks or ratings or purchases, there might be a lot of noise with certain items.

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Yusan Lin, who joined Amazon Fashion this year as an applied scientist, is a coauthor on a RecSys paper called “Denoising self-attentive sequential recommendation”, and it's about that concept: how do you find those items that are potentially harmful to the performance of the system and essentially hide them from the training so that the system learns more of a clean language, if you will, of what people are interested in?

VM: Sometimes the sequence of interactions is way too predictable. In e-commerce, if you think about reordering, where, say, you order the same brand of coffee absolutely every week, there's not really a benefit to recommending that coffee to you, even though it's very accurate. So there's some subtlety in there when we're talking about predicting the next recommendation — the next good recommendation — from a sequence of user interactions.

Fairness

AS: Vanessa, are there any other recent research trends that you find particularly interesting?

VM: In the last, say, 10 years, the attention that researchers have been paying to bias and fairness is tremendously important. As we get better at predicting what people need, and as we become more embedded in everyday life, the effort to make sure that we're not introducing unintended biases is very, very important. It's a hard problem, and I'm very happy to see attention to that.

AS: What kind of approaches do people take to that problem?

VM: The first thing is that the researcher actually has to be aware of the problem. A lot of times the data is very large, and the items you are trying to predict are a very small subset. Suppose that you have a group of people who have blue hair, and they're very interested in products for blue hair. You can imagine they are a tiny, tiny proportion of your data. If your recommender is based on what most people like, you're never going to offer them anything for their blue hair.

It's a class of problems called unknown unknowns, where there’s a small positive class, but you don't know how big it is, and you don't have a way to find that in your data. You know there are some people with blue hair because they've interacted with blue-hair things, but you don't know how many of your customers actually have blue hair.

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Some approaches for that are to sample in a clever way or to create synthetic data or to do domain adaptation, where you have a large amount of known data from some other domain that you can adapt to this new area. For instance, you have a lot of data about people who have green hair, and you can adapt that to people with blue hair.

Another is to look at whether the data itself has a skew in the features. Maybe the features are accidentally correlated, or maybe something is not represented well, because the feature space for the blue haired items is too small. Those are all things to look at.

MH: I totally agree that fairness, along with privacy and explainability, are big topics at this year's RecSys. There definitely is research into news recommendation, which is a big, important topic to the world. There's this idea of filter bubbles, which is a long-hypothesized problem, but one that we're seeing in practice, in which personalization technology makes the range of opinions that we see online shallower and shallower. So for instance, we'll see news that confirms our own beliefs rather than seeing a diversity of viewpoints.

There's some work on those topics at this year's RecSys. One paper in particular I thought was quite interesting because they took a principled approach to looking at what it means for a news article to be diverse. There's a shallow, algorithmic definition of diversity that most prior research has used that may or may not line up with what humans perceive as diversity in news articles.

So they took this more principled approach to measuring diversity using natural-language techniques. They provided a mathematical foundation for measuring the diversity of a set of articles and looked at how different algorithms actually behave on a news dataset. I think that work on fairness is really important and will be very influential in years to come.

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AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. The Generative Artificial Intelligence (AI) Innovation Center team at AWS provides opportunities to innovate in a fast-paced organization that contributes to game-changing projects and technologies leveraging cutting-edge generative AI algorithms. As an Applied Scientist, you'll partner with technology and business teams to build solutions that surprise and delight our customers. We’re looking for Applied Scientists capable of using generative AI and other ML techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. Key job responsibilities - Collaborate with scientists and engineers to research, design and develop cutting-edge generative AI algorithms to address real-world challenges - Work across customer engagement to understand what adoption patterns for generative AI are working and rapidly share them across teams and leadership - Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths for generative AI - Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder - Provide customer and market feedback to Product and Engineering teams to help define product direction. A day in the life Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. What if I don’t meet all the requirements? That’s okay! We hire people who have a passion for learning and are curious. You will be supported in your career development here at AWS. You will have plenty of opportunities to build your technical, leadership, business and consulting skills. Your onboarding will set you up for success, including a combination of formal and informal training. You’ll also have a chance to gain AWS certifications and access mentorship programs. You will learn from and collaborate with some of the brightest technical minds in the industry today. We are open to hiring candidates to work out of one of the following locations: Melbourne, VIC, AUS
AU, NSW, Sydney
Amazon launched the Generative AI Innovation Center (GAIIC) in Jun 2023 to help AWS customers accelerate the use of Generative AI to solve business and operational problems and promote innovation in their organization (https://press.aboutamazon.com/2023/6/aws-announces-generative-ai-innovation-center). GAIIC provides opportunities to innovate in a fast-paced organization that contributes to game-changing projects and technologies that get deployed on devices and in the cloud. As an Applied Science Manager in GAIIC, you'll partner with technology and business teams to build new GenAI solutions that delight our customers. You will be responsible for directing a team of data/research/applied scientists, deep learning architects, and ML engineers to build generative AI models and pipelines, and deliver state-of-the-art solutions to customer’s business and mission problems. Your team will be working with terabytes of text, images, and other types of data to address real-world problems. The successful candidate will possess both technical and customer-facing skills that will allow you to be the technical “face” of AWS within our solution providers’ ecosystem/environment as well as directly to end customers. You will be able to drive discussions with senior technical and management personnel within customers and partners, as well as the technical background that enables them to interact with and give guidance to data/research/applied scientists and software developers. The ideal candidate will also have a demonstrated ability to think strategically about business, product, and technical issues. Finally, and of critical importance, the candidate will be an excellent technical team manager, someone who knows how to hire, develop, and retain high quality technical talent. AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. About the team Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. We are open to hiring candidates to work out of one of the following locations: Sydney, NSW, AUS
US, CA, Sunnyvale
Amazon is looking for a passionate, talented, and inventive Applied Scientists with a strong machine learning background to help build industry-leading Speech and Language technology. Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Automatic Speech Recognition (ASR), Machine Translation (MT), Natural Language Understanding (NLU), Machine Learning (ML) and Computer Vision (CV). As part of our AI team in Amazon AGI, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services that make use of speech and language technology. You will gain hands on experience with Amazon’s heterogeneous speech, text, and structured data sources, and large-scale computing resources to accelerate advances in spoken language understanding. We are hiring in all areas of human language technology: ASR, MT, NLU, text-to-speech (TTS), and Dialog Management, in addition to Computer Vision. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | San Francisco, CA, USA | Seattle, WA, USA | Sunnyvale, CA, USA