Shaped Blog

Explainable AI is increasingly important as it helps users understand the decision-making processes of machine learning models, ensuring fairness, addressing potential biases, and complying with regulations.

This blog post discusses the importance of novelty in recommendation systems and how it can improve user experience by providing diverse and unexpected suggestions.

Let’s go beyond standard machine learning performance measurements and explore at how we can create Serendipity in a high performance recommendation system

If you’re interested in recommendation systems but not sure whether you have enough data this blog post is for you!

Twitter has recently unveiled its open-source recommendation algorithm, aiming to offer users greater transparency into the process through which the platform selects and organizes content for display on their timelines.

Learn how to use Shaped to build a personalized movie recommendation with the classic MovieLens dataset!

You probably have heard of terms like ROC, AUC, and Precision-recall, they show up in data science articles on Medium, machine learning tutorials, and academic papers are full of them. But why are they so important and what do they actually mean? Today we will dive into the specifics of those essential metrics, and explain how they work and their importance in the world of machine learning and recommendation systems.

Shaped is excited to announce that we now support real-time data streaming connectors for Amplitude and Segment.

We’re excited to announce Shaped’s 1.0 release! This launch adds a new level of polish to our APIs, and docs while continuing to improve the performance for your personalization models. It’s now easier than ever to create your own personalization AI using state-of-the-art large language models and real-time streaming technologies.

OpenAI has released its latest natural language processing system, GPT-4, which promises to be even more advanced and capable than its predecessor, GPT-3. GPT-4 is built on the same deep learning approach as the previous models but leverages more data and computation to achieve greater sophistication and accuracy.

Take a look at our discussion on Toolformer — Meta AI's recent approach to fusing large language models (LLM) with external APIs. This might be the start of a new programming paradigm that combines zero-shot machine-learning methodology with traditional software interfaces.

Recently, Meta announced the release of a new AI language generator called LLaMA. While tech enthusiasts have been primarily focused on language models developed by Microsoft, Google, and OpenAI, LLaMA is a research tool designed to help researchers advance their work in the subfield of AI. In this blog post, we will explain how LLaMA is helping to democratize large language models.

Imagine you’re shown two ordered feeds of product recommendations from separate algorithms. In the first one (A) you’re shown: Nike sneakers, Adidas shorts, and an Apple Watch. In the second one (B) you’re shown the order: Apple Watch, Adidas shorts, and Nike Sneakers. -- Which feed is more relevant to you?

Dive into the inner workings of Google AI’s awesome language modeling approach to audio generation!

Shaped is SOC 2 Type 1 certified and compliant, a significant milestone in our journey to provide secure and trustworthy products to customers. This certification demonstrates our commitment to our customer’s data security, providing third-party attestations of our company’s security and compliance standards.

In recent years, Large Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP), enabling significant advancements in language understanding, text generation, and more. With the help of memorization and compositionality capabilities, LLMs can perform various tasks like never before. They're already at the core of several products used by millions of people, such as Google's search engine, Github's Copilot, and OpenAI's ChatGPT!

Dive into the inner workings of TikTok’s awesome real-time recommendation system and learn what makes it one of the best in the field!

Imagine you’re given three movie recommendations from separate algorithms. In the first one (A) you’re given: The Terminator, James Bond, and Star Wars. In the second (B) you’re given: Cars, Toy Story, and Iron Man -- Which recommendation is more relevant to you?

Open-AI released Whisper, an open source speech recognition model with human level robustness and accuracy on English language. Trained on 680,000 hours of multilingual and multitask supervised data collected from the web.

If you haven't been living under a rock for the past few weeks, you've probably heard about the ChatGPT hype and how it's changed how people think of large language models (LLMs). You might not be aware that Microsoft and OpenAI, the firm behind ChatGPT, have been working closely together since 2019 [1]; there are speculations that Microsoft intends to increase its investment by $10 billion [2].

Comparing Shaped with Algolia Recommend

Today we’re excited to announce the release of several APIs and features that help you get the most from your Shaped ranking and recommendation models.

OpenAI just released a new large language model fine-tuned for conversational AI based on gpt-3.5. We tried it out at https://chat.openai.com/chat and asked to write a blog post about itself, the results are amazing!

If you’ve ever browsed TikTok’s For You Page, Facebook Newsfeed, Instagram Reels, Youtube Shorts, or any “infinite” scrolling social feed you have been served personalized recommendations. These feeds use algorithms to find out what content will keep you browsing the feed. I’ve seen many people complain about how their personalized feeds get worse over time, recommending the same topic over and over, old content, or even things you already consumed.

What if I told you that your web browsing is being modeled as a language in most of the web pages you visit? Let me show you why and how.

The last day at RecSys 2022 started with a session on Sessions and Interaction, moved on to Models and Learning to finish with Large-Scale Recommendations. Here are our favorite 5 papers and talks of the day.

It’s been another fantastic day at RecSys 2022. Following the Women in RecSys Breakfast, the day started with a keynote from Catherine D’Ignazio and then throughout the day had the following sessions: Fairness & Privacy, Diversity & Novely, and Models and Learning I. Here are our favorite 5 papers and talks.

We just wrapped up a fantastic first day at Recsys2022 in Seattle. If you see the Shaped team come say hi. Here are our favorite 5 papers and talks of the day.

Comparing Shaped with AWS Personalize

The field of artificial intelligence is growing at an extraordinary speed. Check out some selected topics that we think AI will affect and be a big factor in the upcoming years.

Introducing Shaped's "Model API". A configuration API to Shaped's multi-stage recommendation system.

Problems with bias in recommendation systems and what you can do about them.

How could machines learn as efficiently as humans and animals?

A little less analytics, a little more action please.

Not sure about the differences between information retrieval systems and recommender systems? Don't worry, we got you covered.

This year’s recommender systems summit included a great set of speakers, each one sharing unique learnings on working with recommender systems.

Data quality and volume is what makes rankings algorithms at big-tech so seamless. How can you create the same experiences with the data you have? Data-centric AI may be the answer!

We just released Shaped's API docs at: docs.shaped.ai

From Search based to Discovery first

Shaped is proud to announce our $1.9 million seed round!

We just presented to 1500 investors at Demo Day W22!

We’re excited to announce that Shaped is backed by Y Combinator as part of its Winter 2022 batch!