Digitec Galaxus AG is the biggest online retailer in Switzerland, operating two online stores: Digitec, Switzerland’s online market leader for consumer electronics and media products, and Galaxus, the largest Swiss online shop with a steadily growing range of consistently low-priced products for almost all daily needs. 

Known for its efficient, personalized shopping experiences, it’s clear that Digitec Galaxus understands what it takes to deliver a platform that is interesting and relevant to customers every time they shop. 

The problem: Personalizing decisions for every situation

Digitec Galaxus already had established an engine to help them personalize experiences for shoppers when they reached out to Google Cloud. They had multiple recommendation systems in place and were also extensive early adopters of Recommendations AI, which already enabled them to offer personalized content in places like their homepages, product detail pages, and their newsletter. 

But those same systems sometimes made it difficult to understand how best to combine and optimize to create the most personalized experiences for their shoppers. Their requirements were threefold:

1. Personalization: They have over 12 recommenders they can display on the app, however they would like to contextualize this and choose different recommenders (which in turn select the items) for different users. Furthermore they would like to exploit existing trends as well as experiment with new ones.
2. Latency: They would like to ensure that the solution is architected so that the ranked list of recommenders can be retrieved with sub 50 ms latency.
3. End-to-end easy to maintain & generalizable/modular architecture: Digitec wanted the solution to be architected using an easy to maintain, open source stack, complete with all MLops capabilities required to train and use contextual bandits models. It was also important to them that it is built in a modular fashion such that it can be adapted easily to other use cases which have in mind such as recommendations on the homepage, Smartags and more . 

To improve, they asked us to help them implement a machine learning (ML) contextual bandit based recommender system on Google Cloud taking all the above factors into consideration to take their personalization to the next level. 

Contextual bandits algorithms are a simplified form of reinforcement learning and help aid real-world decision making by factoring in additional information about the visitor (context) to help learn what is most engaging for each individual. They also excel at exploiting trends which work well, as well as exploring new untested trends which can yield potentially even better results. For instance, imagine that you are personalizing a homepage image where you could show a comfy living room couch or pet supplies. 

Without a contextual bandit algorithm, one of these images would be shown to someone at random without considering information you may have observed about them during previous visits. Contextual bandits enable businesses to consider outside context, such as previously visited pages or other purchases, and then observe the final outcome (a click on the image) to help determine what works best. 

Creating a personalization system with contextual bandits

While Digitec Galaxus heavily personalizes their website homepages, they are very very sensitive and also require more cross-team collaboration to update and make changes. 

Together with the Digitec Galaxus team, we decided to narrow the scope and focus on building a contextual bandit personalization system for the newsletter first. The digitec Galaxus team has complete control over newsletter decisions and testing various ML experiments on a newsletter would have less chance of adverse revenue impact than a website homepage. 

The main goal was to architect a system that could be easily ported over to the homepage and other services offered by Digitec with minimal adaptations. It would also need to satisfy the functional and non-functional requirements of the homepage as well as other internal use cases.

Below is a diagram of how the newsletter’s personalization recommendation system works:

• The system is given some context features about the newsletter subscriber such as their purchase history and demographics. Features are sometimes referred to as variables or attributes, and can vary widely depending on what data is being analyzed. 
• The contextual bandit model trains recommendations using those context features and 12 available recommenders (potential actions). 
• The model then calculates which action is most likely to enhance the chance of reward (a user clicking in the newsletter) and also minimize the problem (an unsubscribe). 

Calculating whether a click was a newsletter or an unsubscribe enabled the system to optimize for increasing clicks and avoid showing non-relevant content to the user (click-bait). This enabled Digitec Galaxus to exploit popular trends while also exploring potentially better-performing trends. 

How Google Cloud helps

The newsletter context-driven personalization system was built on Google Cloud architecture using the ML recommendation training and prediction solutions available within our ecosystem. 

Below is a diagram of the high-level architecture used:

The architecture covers three phases of generating context-driven ML predictions, including: 

ML Development: Designing and building the ML models and pipeline 
Vertex Notebooks are used as data science environments for experimentation and prototyping. Notebooks are also used to implement model training, scoring components, and pipelines. The source code is version controlled in Github. A continuous integration (CI) pipeline is set up to automatically run unit tests, build pipeline components, and store the container images to Cloud Container Registry. 

ML Training: Large-scale training and storing of ML models 
The training pipeline is executed on Vertex Pipelines. In essence, the pipeline trains the model using new training data extracted from BigQuery and produces a trained, validated contextual bandit model stored in the model registry. In our system, the model registry is a curated Cloud Storage. 

The training pipeline uses Dataflow for large scale data extraction, validation, processing, and model evaluation, and Vertex Training for large-scale distributed training of the model. AI Platform Pipelines also stores artifacts, the output of training models, produced by the various pipeline steps to Cloud Storage. Information about these artifacts are then stored in an ML metadata database in Cloud SQL. To learn more about how to build a Continuous Training Pipeline, read the documentation guide.

ML Serving: Deploying new algorithms and experiments in production 
The training pipeline uses batch prediction to generate many predictions at once using AI Platform Pipelines, allowing Digitec Galaxus to score large data sets. Once the predictions are produced, they are stored in Cloud Datastore for consumption. The pipeline uses the most recent contextual bandit model in the model registry to evaluate the inference dataset in BigQuery and give a ranked list of the best newsletters for each user, and persist it in Datastore. A Cloud Function is provided as a REST/HTTP endpoint to retrieve the precomputed predictions from Datastore.

All components of the code and architecture are modular and easy to use, which means they can be adapted and tweaked to several other use cases within the company as well.

Better newsletter predictions for millions

The newsletter prediction system was first deployed in production in February, and Digitec Galaxus has been using it to personalize over 2 million newsletters a week for subscribers. The results have been impressive, 50% higher than our baseline. However, the collaboration is still ongoing to improve the results even more. 

“Working at this level in direct exchange with Google’s machine learning experts is a unique opportunity for us. The use of contextual bandits in the targeting of our recommendations enables us to pursue completely new approaches in personalization by also personalizing the delivery of the respective recommender to the user. We have already achieved good results in our newsletter in initial experiments and are now working on extending the approach to the entire newsletter by including more contextual data about the bandits arms. Furthermore, as a next step, we intend to apply the system to our online store as well, in order to provide our users with an even more personalized experience. To build this scalable solution, we are using Google’s open source tools such as TFX and TF Agents, as well as Google Cloud Services such as Compute Engine, Cloud Machine Learning Engine, Kubernetes Engine and Cloud Dataflow.”—Christian Sager, Product Owner, Personalization ( Digitec Galaxus)

Since the existing architecture and system is also dynamic, it will automatically adapt to new behaviours, trends, and users. As a result, Digitec Galaxus plans to re-use the same components and extend the existing system to help them improve the personalization of their homepage and other current use cases they have within the company. Beyond clicks and user engagement, the system’s flexibility also allows for future optimization of other criteria. It’s a very exciting time and we can’t wait to see what they build next!

Verve Group is an ecosystem of demand and supply technologies fusing data, media, and technology to deliver results and growth to both advertisers and publishers – no matter the screen or location, no matter who, what, or where a customer is. Classifying massive amounts of this unstructured data at scale is the first step in helping to surface relevant, high-quality content to users—and that’s where natural language processing (NLP) comes in.

Verve Group uses the NLP API from Google Cloud’s Vertex AI to fetch data for their internal content classification quality verification and as an additional source for building categorization models. By leveraging the NLP API’s Content Classification models, which are now generally available and offer Google’s latest large language model (LLM) technology, Verve Group powers classification through an updated and expanded training data set with over 1,000 labels and support for 11 languages (Chinese, French, German, Italian, Japanese, Korean, Portuguese, Russia, Spanish, and Dutch join previously-available English). 

Verve Group has been using Google Cloud’s NLP API since day one, because of both the ease of implementation and the quality compared to competing NLP products. With documentation that is “comprehensive and self-explanatory,” the NLP API “allows for fast adoption and implementation, from test models all the way to production,” said Rami Alanko, GM of Verve Group.

Leveraging the NLP API has facilitated Verve Group’s fast go-to-market motions by enabling its customers to quickly discover and classify new content. “Operating on a global level with tens of different regions and languages, we have still been able to maintain high quality for our product and high retention rates with our clients,” Rami shared. “In a recent client case, we achieved 82% improvement in CTR when optimized with content quality measurements enabled by the API. In another client case, we drove brand safety risk down to 0.16% from 4% thanks to classification quality. Along with the new functionalities of the Google NLP, I can only see this trend continuing to strengthen.”

Verve Group is excited to further expand their NLP use cases by leveraging the new Content Classification models, which have already helped them expand their classification inventory, improve the quality and performance of their quality verification for customers, and unlock new use cases for NLP. “Our classification model accuracy improved 41% using Google NLP as a verification partner,” said Rami.  

Additionally, Verve Group is now using the API for metadata analysis on a  large image database. “We browse the database and run the image metadata via our classification. This flow enables us to classify images reliably aligned with our standard classification.  We pretty much use the same data flow for our runtime in-app textual content analysis, therefore allowing for close to real-time consumer engagement,” Rami added.

To learn more about how companies are leveraging NLP API from Google Cloud Vertex AI, click here, and to learn more about Google Cloud’s work with foundation models and generative AI, read The Prompt on Transform with Google Cloud.

Natural language understanding (NLU) is getting increasingly better at solving complex problems and these language breakthroughs are creating big waves in Artificial Intelligence. For example, new language models are enabling Everyday Robots to create more helpful robots that can break down user instructions and have even enabled people to generate imaginative visuals from complex text prompts. 

These leaps in NLU are powered by neural networks trained to understand human language. This technology has greatly advanced since the introduction of Google’s Transformer architecture in 2017 with the introduction of large models trained on massive amounts of data like GPT-3 and, even more recently, with GLaM, LaMDA, and PaLM. This latest generation of models are called Large Language Models (LLMs) because of their sheer size and the vast volumes of data on which they are trained, and they can be applied to a range of tasks to create more powerful digital assistants, generate better search results and product recommendations, enforce smarter platform curation and safety features, and much more. 

For these reasons, we’re pleased to announce we’ve updated the Google Cloud Natural Language (NL) API with a new LLM-based model for Content Classification. 

With an expansive pre-trained classification taxonomy, the newest version of Content Classification from the Natural Language API leverages the latest Google research to improve customer use cases spanning actionable insights on user trends, to ad targeting, to content-based filtering. In this article, we’ll explore the NL API’s new capabilities, which are the first of many efforts we’ll be making to bring the power of LLMs to Google Cloud. 

How LLMs help machines understand human language 

As Google Cloud VP and General Manager of AI and Industry Solutions, Andrew Moore has argued, if computer systems become more conversant with natural human languages, they become a foundation for more sophisticated use cases, able to not only understand user intent but also create complex bespoke solutions. Google has been a leading research force in this space, with LLM projects like LaMDA, PaLM and T5 contributing to the Cloud NL API’s improved v2 classification model.

Parsing language is a difficult AI task for machines due in part to the contextual and individual interpretation of words or phrases. The word “server,” for example, could refer to a computer, a restaurant employee, or a tennis player. To understand the word, a model needs to be trained around not only a basic definition but also the context and positioning of the word within a sentence or conversation and its evolving connotations. Because they process voluminous training data via Transformers, LLMs are well-suited to this type of work. 

Thanks to the integration of Google’s latest language modeling technology, and an updated and expanded training data set, the next generation of the Content Classification API not only has over 1,000 labels (up from around 600 previously), but now also supports 11 languages (with Chinese, French, German, Italian, Japanese, Korean, Portuguese, Russia, Spanish, and Dutch joining previously-available English)—and does so with improved accuracy.

​​AI raises questions about the best way to build fairness, interpretability, privacy, and security into these new systems in order to benefit people and society. At Google, we prioritize the responsible development of AI and take steps to offer products where a responsible approach is built in by design. For Content Classification, we limited use of sensitive labels and conducted performance evaluations. See our Responsible AI page for more information about our commitments to responsible innovation. 

Get Started 

Today’s announcement is just the first step in bringing LLM capabilities to Google Cloud AI products, and we’re excited to see how our more powerful Natural Language API helps developers, analysts and data scientists generate insights and offer superior experiences. Our early adopters are implementing the API to improve user recommendations, display ad targeting, and insights about new trends.

If you’re ready to get started with this major leap in Google Cloud language services, visit our NL API documentation, and to learn more about Google Cloud’s AI services, visit our AI and machine learning products page.