# TensorFlow Recommenders Published articles for TensorFlow Recommenders. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Augmenting recommendation systems with LLMs DevFeed: [Augmenting recommendation systems with LLMs]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-06-06T21:00:00Z Content type: tutorial Language: en Sources: [The TensorFlow Blog]() Topics: [Language models](), [AI Chat]() Tags: [large-language-models](), [llm](), [llms](), [palm-api](), [recommendation-systems](), [tensorflow](), [tensorflow-recommenders]() ### AI overview A practical tutorial on using LLMs and the PaLM API to add conversational and sequential recommendation capabilities to retrieval-ranking recommendation systems. ### Source excerpt Posted by Wei Wei, Developer Advocate Large language models (LLMs) are taking the world by storm, thanks to their powerful ability to generate text, translate languages, and answer questions in a coherent and informative way. At Google I/O 2023, we released the PaLM API as 'public preview' so that many developers can start building apps with it. While PaLM API already has excellent documentation on its extensive usage and best practices, in this blog we are going to take a more focused approach to explore how to leverage LLMs to augment your ML systems in a practical application: recommendation systems. As a refresher, modern recommendation systems often follow a retrieval-ranking architecture, which enables them to effectively and efficiently filter and rank relevant items to maximize the utility in production. You can go through this codelab to learn about building a fullstack movie recommendation system using TensorFlow and Flutter. We will discuss how LLMs can be incorporated into this retrieval-ranking pipeline. Conversational recommendations If you already have access to Bard, you can ask it to create recommendations for you interactively in a dialogue. Here is an example of asking Bard for movie recommendations: As a developer, you can build a similar functionality in your own applications, using the PaLM API Chat service with minimal effort: prompt = """You are a movie recommender and your job is to recommend new movies based on user input. So for user 42, he is in the mood for some drama movies with artistic elements tonight. Could you recommend three? Output the titles only. Do not include other text.""" response = palm.chat(messages=prompt) print(response.last) # Sure, here are three drama movies with artistic elements that I recommend for user 42: # # 1. The Tree of Life (2011) # 2. 20th Century Women (2016) # 3. The Florida Project (2017) # # I hope you enjoy these movies! The PaLM API also allows you to help your user continue the exploration and inter ## Attend our first Developer Summit on Recommendation Systems DevFeed: [Attend our first Developer Summit on Recommendation Systems]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2023-05-26T14:30:00Z Content type: news Language: en Sources: [The TensorFlow Blog]() Topics: [recommendation systems](), [Language models](), [TensorFlow Agents]() Tags: [developer](), [event](), [generative-ai](), [large-language-models-llms](), [recommendation-systems](), [tensorflow-agents](), [tensorflow-ranking](), [tensorflow-recommenders]() ### AI overview Announcement of an online Developer Summit on Recommendation Systems covering TensorFlow recommendation products, LLM-augmented recommenders, and generative retrieval research. ### Source excerpt Posted by Wei Wei, Developer Advocate Register for the Summit here! Recommendation systems are everywhere. They power our favorite websites, apps, and services, helping us find the things we enjoy. But how do modern recommenders work? What are the key components and how do they fit together? How can we make them even better? Since we launched our recommendation system landing page last year, we have heard many positive feedback from our developer community. While many developers find the new consolidated page very useful to get started with our suite of products, they are also eager to learn more about how to best leverage them to build powerful in-house recommenders for their own business needs. This is why we are very excited to announce our first-ever Developer Summit on Recommendation Systems (registration is open now). This event will be held online on June 9, 2023 10AM - 12:15PM US Pacific Time and it will bring together many Google engineers who authored our suite of products to share their insights and expertise in recommendation systems. At this summit, we will not only cover specific products (such as TensorFlow Recommenders, TensorFlow Ranking, and TensorFlow Agents), share ideas on augmenting recommenders with Large Language Models (LLMs), but also discuss Google's cutting edge recommendation system research (e.g., generative retrieval using generative AI techniques). This Developer Summit is the perfect event for anyone who wants to learn more about recommendation systems. Whether you're just getting started or a seasoned practitioner in this exciting domain, you're sure to find something valuable at this event. We look forward to (virtually) meeting you there!