# recommendation systems Published articles for recommendation systems. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 🍔🧠 Pinterest's Fix for the Hardest Problem in ML Infra DevFeed: [🍔🧠 Pinterest's Fix for the Hardest Problem in ML Infra]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-09-14T15:31:30Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [recommendation systems](), [Feature Engineering](), [Inference](), [Streaming](), [configuration](), [Framework](), [data](), [Low Latency]() Tags: [batch](), [configuration](), [data](), [feature-engineering](), [low-latency](), [ml](), [pinterest](), [ranking](), [recommendation-systems](), [streaming]() ### AI overview Pinterest redesigned its user-sequence platform for ranking, retrieval, and recommendation systems by defining signals once and instantiating them consistently across streaming, batch, and serving workloads. The approach uses Python configuration with validated schemas, a shared execution engine, cooperating streaming and batch paths, and columnar time-partitioned storage to improve freshness, completeness, consistency, and operational efficiency. ### Source excerpt PLUS: OpenAI agents beat math 🧮, Test techniques for agents ⚡, Postgres survival guide 📖 ## How Sprig Replaced Postgres, ClickHouse & Redis...with 4-8x Better Latency DevFeed: [How Sprig Replaced Postgres, ClickHouse & Redis...with 4-8x Better Latency]() Original publisher: [Read original article]() Author: Cynthia Dunlop Published: 2026-08-24T13:30:56Z Content type: article Language: en Sources: [ScyllaDB]() Topics: [Latency](), [Low Latency](), [Databases](), [PostgreSQL](), [clickhouse](), [Redis](), [real-time](), [Back end](), [recommendation systems](), [Artificial Intelligence]() Tags: [backend](), [clickhouse](), [data](), [databases](), [engineering](), [latency](), [low-latency](), [postgres](), [postgresql](), [real-time](), [recommendation-systems](), [redis](), [user-stories]() ### AI overview Sprig outgrew PostgreSQL as its AI-powered product research platform scaled to more than 1.3 trillion events, 75 billion attributes, and high-volume real-time processing. The article describes its database challenges and the path toward lower-latency data infrastructure involving PostgreSQL, ClickHouse, and Redis. ### Source excerpt With ScyllaDB, a small engineering team could focus on building their product instead of battling their databases. ## From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta's Ads Ranking DevFeed: [From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta's Ads Ranking]() Original publisher: [Read original article]() Author: Steven De Gryze; Parshva Doshi; Sean O'Byrne; Arnold Overwijk; Dinesh Ramasamy; Lee Xiong Published: 2026-08-05T19:20:20Z Content type: article Language: en Sources: [Engineering at Meta](), [Meta ML Applications]() Topics: [recommendation systems](), [Temporal data]() Tags: [ads](), [architecture](), [data-infrastructure](), [meta](), [ml-applications](), [production-engineering](), [recommendation-systems](), [scaling-laws](), [tokenization]() ### AI overview Meta describes a multi-stage sequence-model architecture for ads ranking that separates offline user modeling from lightweight online ranking. It also uses dense tokenization and target-aware attention to learn feature interactions, with reported conversion and ad-click lifts across Instagram and Facebook. ### Source excerpt Every day, Meta's recommendation platforms handle billions of user interactions, generating rich temporal signals that capture individual preferences and intent across products, ads, and content. In our 2024 post on sequence learning for ads recommendations, we showed how modeling the order and timing of user actions (rather than relying on static, manually engineered sparse features) [...] Read More... The post From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta's Ads Ranking appeared first on Engineering at Meta. ## GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model DevFeed: [GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model]() Original publisher: [Read original article]() Author: Darren Liu; Huayu Li; Raghav Boinepalli; Yuzhen Huang; Jackie (Jiaqi) Xu; Richard Qiu; Chunzhi Yang; Rich Zhu; Dev (Devashish) Shankar; Huaqing Xiong Published: 2026-08-03T18:00:17Z Content type: article Language: en Sources: [Engineering at Meta](), [Meta AI Research](), [Meta ML Applications]() Topics: [recommendation systems](), [networking]() Tags: [ads](), [ai-infrastructure](), [ai-research](), [data-infrastructure](), [gpu](), [hardware](), [kernels](), [llm](), [meta](), [ml-applications](), [networking](), [recommendation-systems](), [training]() ### AI overview Meta describes training its GEM ads recommendation foundation model at LLM scale. The article covers recommendation-specific kernels, ultra-low-precision training, and topology-aware parallelism that doubled end-to-end training efficiency to 20-25% MFU while increasing training FLOPs fourfold. ### Source excerpt Meta's Generative Ads Recommendation Model (GEM), the foundation model behind ads recommendations across Instagram and Facebook, now trains at LLM scale on several thousand of the latest-generation GPUs. This post goes into the details on how we achieved: doubling end-to-end (E2E) training efficiency to 20-25% Model FLOPs Utilization (MFU) while scaling training FLOPs 4x in [...] Read More... The post GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model appeared first on Engineering at Meta. ## SilverTorch: Index as Model -- A New Retrieval Paradigm for Recommendation Systems DevFeed: [SilverTorch: Index as Model -- A New Retrieval Paradigm for Recommendation Systems]() Original publisher: [Read original article]() Author: Lei Chen; Yiyi Pan; Ivy Sun; Sha Meng; Cornelia Carapcea; Shilin Ding; Ram Ramanathan; Nipun Mathur; Hong Yan; Lars Backstrom Published: 2026-05-26T16:00:01Z Content type: article Language: en Sources: [Meta ML Applications]() Topics: [recommendation systems](), [Neural Network](), [Architecture & Design](), [systems]() Tags: [compute](), [efficiency](), [engineering](), [meta](), [ml-applications](), [model](), [recommendation-systems](), [research](), [retrieval](), [systems]() ### AI overview Meta introduces SilverTorch, a unified model-based retrieval system for recommendation systems. It replaces separate retrieval microservices with modules in a single neural network, allowing more complex modeling and more candidates to be evaluated within a sub-100-millisecond target. The article reports up to 23.7x higher throughput and 20.9x greater compute cost efficiency than specified baseline approaches. ### Source excerpt We're introducing SilverTorch, a reimagining of recommendation systems that unifies all retrieval components for user generated content under a unified architecture. SilverTorch shows up to 23.7x higher throughput compared to the state-of-the-art approaches. It's also showing 20.9x more compute cost efficiency compared to a CPU-based solution while also improving accuracy. Our research paper, "SilverTorch: A [...] Read More... The post SilverTorch: Index as Model -- A New Retrieval Paradigm for Recommendation Systems appeared first on Engineering at Meta. ## Making User-Sequence Data More Cost-Efficient, Faster, and Easier to Use DevFeed: [Making User-Sequence Data More Cost-Efficient, Faster, and Easier to Use]() Original publisher: [Read original article]() Author: Pinterest Engineering Published: 2026-05-21T16:01:00Z Content type: article Language: en Sources: [Pinterest Engineering Blog - Medium]() Topics: [data](), [recommendation systems](), [datasets](), [Machine learning](), [Embeddings](), [Feature Engineering](), [Inference](), [Latency](), [systems]() Tags: [data](), [data-infrastructure](), [datasets](), [embeddings](), [engineering](), [feature-engineering](), [inference](), [latency](), [machine-learning](), [pinterest](), [production](), [recommendation-system](), [recommendation-systems](), [retrieval](), [systems](), [train](), [transformers]() ### AI overview Pinterest describes a redesign of its user-sequence platform for ranking, retrieval, and recommendation workloads. The article explains how enriched event sequences support training datasets, offline analysis, online inference, and latency-sensitive production use cases, with goals of reducing cost, improving extensibility, and simplifying debugging. ### Source excerpt Authors (listed alphabetically) Ads Feature Engineering Infra team: Ajay Venkatakrishnan, Le Zhang Core ML Infra team: Eric Shang, Pihui Wei ML Data team: Connor Votroubek, Yi He User Understanding team: Camilo Munoz, Simin Li If you work on ranking, retrieval, or recommendation systems, you've probably asked for some version of the same thing: "Give me the last N meaningful actions this user took, with the right enrichments, in a format that's easy to train and serve ML models." On paper, that sounds simple. In practice, "user sequences" often become one of the most expensive and fragile parts of the ML data stack. They end up powering everything from training datasets to offline analysis and online inference, so they need to be fresh and complete at the same time. They must remain consistent as you add new events and enrichments. And they have to do all of this while serving latency-sensitive production workloads. This article walks through how we redesigned our user-sequence platform to make these sequences cheaper to run, faster to extend, and easier to debug, while still supporting demanding production use cases. What We Mean by "User Sequence" In this context, a user sequence is an ordered list of recent, relevant events for a user, along with the enrichments (signals) attached to each event. Here, enrichments mean all the extra signals we attach to raw events, so they're useful for models: embeddings (for example, Pin or query representations), contextual features (such as surface, device, or country), and derived attributes or counters that describe how the user interacted with a piece of content over time. A concrete example helps. Imagine a sequence made up of the last 500 engagements a user had with Pinterest Pins. Each event in that sequence might carry a timestamp, an action type, the surface where the action occurred, and a handful of embedding features or categorical attributes. As a data primitive, user sequences are powerful. They capture temporal b ## How Amazon Uses LLMs and COSMO to Improve Product Recommendations DevFeed: [How Amazon Uses LLMs and COSMO to Improve Product Recommendations]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-05-04T15:30:20Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [amazon](), [recommendation systems](), [Large Language Model](), [data]() Tags: [amazon](), [data](), [knowledge-graphs](), [llm](), [products](), [recommendation-systems]() ### AI overview The article explains how Amazon built COSMO, a commonsense knowledge graph for product recommendations. It describes using an LLM to generate candidate explanations from shopping behavior, filtering and evaluating those candidates, deriving a structured ontology, and distilling the result into COSMO-LM. ### Source excerpt PLUS: How Shazam Works 🎼 , Agent Harness 🧇, Vercel Breach 🔥 ## AI agent reliability requires recovery infrastructure for partial failures DevFeed: [AI agent reliability requires recovery infrastructure for partial failures]() Original publisher: [Read original article]() Author: Melanie Warrick Published: 2026-04-01T00:00:00Z Content type: opinion Language: en Sources: [Temporal Blog]() Topics: [Artificial Intelligence](), [reliability](), [Resilience](), [agent observability](), [Machine learning](), [coding assistant](), [Neural Network](), [image recognition](), [Google](), [recommendation systems]() Tags: [agent](), [agents](), [ai](), [ai-agents](), [coding-assistant](), [google](), [image-recognition](), [neural-network](), [recommendation-systems](), [reliability](), [resilience](), [temporal-voices]() ### AI overview AI agents can reason through complex tasks and run long workflows, but failures during execution can compound when systems cannot checkpoint progress, recover from partial failures, or resume work. The article argues that dependable AI requires durable infrastructure and resilience in addition to better models, placing current agent reliability challenges in the context of earlier AI failures. ### Source excerpt Smart AI agents still fail mid-workflow. Learn why solving the AI reliability gap requires durable infrastructure, not just better models. ## Our Early Journey to Transform Instacart's Discovery Recommendations with LLMs DevFeed: [Our Early Journey to Transform Instacart's Discovery Recommendations with LLMs]() Original publisher: [Read original article]() Author: Moein Hasani Published: 2026-02-26T18:55:35Z Content type: article Language: en Sources: [Instacart]() Topics: [recommendation systems](), [LLMs](), [Generative AI](), [personalization](), [User Experience](), [Artificial Intelligence]() Tags: [ai](), [discovery](), [generative-ai](), [instacart](), [large-language-models](), [llm](), [llms](), [machine-learning](), [personalization](), [recommendation-systems](), [recommendations](), [recommender-systems](), [systems](), [user-experience]() ### AI overview Instacart describes its early effort to use large language models in the Shopping Hub, an app surface for personalized product discovery. The article covers an AI-native platform for content generation, evaluation, and retrieval, and reports that generative models show promise for improving recommendations at scale. ### Source excerpt Key Contributors: Moein Hasani, Hamidreza Shahidi, Trace Levinson, Guanghua Shu Introduction At Instacart, we are laser-focused on improving the user experience by making shopping feel easy, engaging, and personalized. Our discovery surfaces play a central role in bringing this to life. Alongside explicit Search intents, discovery is our opportunity to meet customers' implicit needs, presenting them with the most relevant and inspiring content we have to offer. The main discovery surface within the Instacart app, referred to here as the "Shopping Hub", is one of the most critical in this regard. This is the surface a customer lands on within the Instacart app after selecting their desired retailer, guiding them along their entire journey. What users see here shapes not just what they buy, but how intuitive and enjoyable their experience feels. Given its importance, our team runs dozens of Shopping Hub experiments per year, constantly evaluating new ways to enrich the discovery experience. Historically, these experiments have been constrained by static content libraries feeding our recommendation systems. With the rapid advancement of generative AI, a critical opportunity began to emerge: rather than incrementally improving a swath of legacy systems, could we leverage LLMs to rethink how content shows up for a user from the ground up? Which new primitives could we build to uplevel quality, personalization, and cohesion across the page? This blog post walks through our early journey to answer these questions. By investing in a new AI-native platform for content generation, evaluation, and retrieval, we have found generative models to show real promise in improving recommendations at scale. Below, we highlight the approach we took in developing this platform, a few key learnings so far, and where we're most bullish moving forward. Limitations of Traditional Recommendation Engines Our Shopping Hub page is constructed from multiple subcomponents called placements. Each p ## The Sora feed philosophy DevFeed: [The Sora feed philosophy]() Original publisher: [Read original article]() Published: 2026-02-03T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [recommendation systems](), [personalization](), [Algorithms](), [data](), [ChatGPT]() Tags: [algorithms](), [chatgpt](), [data](), [personalization](), [product](), [recommendation-systems](), [safety]() ### AI overview The Sora feed philosophy describes a personalized recommendation system designed to favor creativity, active participation, connection, safety, and user control. Ranking may use activity, ChatGPT history, engagement, author, location, and safety signals, with parental controls for personalization and continuous scrolling. ### Source excerpt Discover the Sora feed philosophy--built to spark creativity, foster connections, and keep experiences safe with personalized recommendations, parental controls, and strong guardrails. ## Introducing RTEB: A New Standard for Retrieval Evaluation DevFeed: [Introducing RTEB: A New Standard for Retrieval Evaluation]() Original publisher: [Read original article]() Author: Frank Liu; Kenneth Enevoldsen; Solomatin Roman; Isaac Chung; Tom Aarsen; Fődi, Zoltán Published: 2025-10-01T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Benchmark](), [LLM evaluation / benchmarking](), [Retrieval Augmented Generation (RAG)](), [generalization in machine learning](), [recommendation systems]() Tags: [artificial-intelligence](), [benchmark](), [benchmarks](), [community](), [datasets](), [developers](), [embedding](), [evaluation](), [models](), [nlp](), [open](), [performance](), [rag](), [recommendation-systems](), [research](), [retrieval](), [search](), [zero-shot]() ### AI overview Hugging Face introduces the beta Retrieval Embedding Benchmark (RTEB), designed to evaluate the retrieval accuracy and generalization of embedding models in real-world applications. It combines open and private datasets to provide a fairer, more transparent, application-focused evaluation standard. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## exploits.club Weekly Newsletter 87 - NVIDIA Merlin Bugs, GrapheneOS's Allocator, Intel CPU Bugs, And More DevFeed: [exploits.club Weekly Newsletter 87 - NVIDIA Merlin Bugs, GrapheneOS's Allocator, Intel CPU Bugs, And More]() Original publisher: [Read original article]() Author: exploits.club Published: 2025-09-26T15:00:49Z Content type: news Language: en Sources: [exploits.club]() Topics: [Nvidia](), [GrapheneOS](), [Exploit](), [intel](), [PyTorch](), [recommendation systems](), [GPU]() Tags: [exploit](), [intel](), [nvidia](), [pytorch](), [rca](), [recommendation-systems](), [updates]() ### AI overview The 87th exploits.club weekly newsletter reviews developer and security news, including a remote code execution vulnerability in NVIDIA Merlin Transformers4Rec, GrapheneOS's hardened memory allocator, and an Intel GPU driver crash during power transitions. It also mentions Google Cloud VRP updates and other security resources. ### Source excerpt Happy Friday. Almost that time of the week again: Annnnnyway 👇 In Case You Missed It... Google Cloud VRP: Enhancing Transparency and Impact in Our Rewards Program - Some VRP updates for Google Cloud with more transparency and consistency, less ambiguity New chITchat by pamoutaf Episode - A chat with @PinkDraconian that ## Inside Glassdoor's Multi-Stage Recommendation System DevFeed: [Inside Glassdoor's Multi-Stage Recommendation System]() Original publisher: [Read original article]() Author: Prateek Shekhar Published: 2025-08-12T19:37:39Z Content type: article Language: en Sources: [Glassdoor Engineering]() Topics: [recommendation systems](), [recommendations](), [personalization](), [Algorithms](), [ordering](), [ann]() Tags: [algorithms](), [ann](), [glassdoor](), [ordering](), [personalization](), [recommendation-system](), [recommendation-systems](), [recommender-systems]() ### AI overview This article gives a high-level overview of Glassdoor's hybrid, multi-stage recommendation system. It describes how the system uses user behavior, similar-user behavior, and item features to produce personalized recommendations for the Community Feed, Email, and Push notifications, with stages including candidate generation, filtering, scoring, and ordering. ### Source excerpt Introduction: Recommendation systems are central to modern digital platforms, delivering personalized content by intelligently analyzing user behavior and preferences through advanced algorithms. Widely used in e-commerce, streaming services, social media, and online learning, they drive user satisfaction, engagement, and retention while supporting business growth. Typically, these systems operate using one or more of the following approaches: Collaborative Filtering: This approach examines user behavior by comparing how users interact with items. It identifies patterns by analyzing similar user actions. For example, if two users have watched or purchased similar items, the system will recommend those same items to other users with similar behavior. Content-Based Filtering: In this approach, the system analyzes the characteristics of the items themselves, such as text, images, or other features. It recommends items that have features similar to those a user has previously liked. For example, if a user enjoyed books with specific keywords, the system will suggest books with those exact keywords. Hybrid Approach: This approach combines collaborative and content-based filtering to enhance personalization and accuracy. It utilizes user behavior patterns in conjunction with item features, offering more personalized and accurate recommendations. At Glassdoor, our Community Feed, Email, and Push notifications are powered by a recommendation system that follows a hybrid approach. It analyzes individual users' behavior, such as posts, comments, and reactions, as well as the behavior of similar users, to deliver tailored, personalized recommendations. This post presents a high-level overview of the multi-stage architecture behind our system, highlighting candidate generation, filtering, scoring, ordering, and supporting infrastructure. Glassdoor's Recommendation System ArchitectureFigure 1: Glassdoor's Recommendation System Architecture Glassdoor's Recommendation System follo ## Scaling recommendations service at OLX DevFeed: [Scaling recommendations service at OLX]() Original publisher: [Read original article]() Author: Jordi Esteve Sorribas Published: 2025-07-08T15:03:23Z Content type: article Language: en Sources: [OLX]() Topics: [FastAPI](), [recommendation systems](), [Latency](), [Back end](), [Python](), [distributed-systems](), [async]() Tags: [async](), [backend](), [concurrency](), [distributed-systems](), [fastapi](), [latency](), [machine-learning](), [optimization](), [python](), [recommendation-systems](), [recommendations](), [scalability]() ### AI overview This article describes how OLX scaled a Python FastAPI recommendations service to handle tens of thousands of requests per second with p99 latency below 10 ms. It discusses the service's ownership, data sources, and the use of asynchronous non-blocking I/O. ### Source excerpt Optimizing FastAPI at Scale: Lessons from OLX's Recommendation PlatformPhoto by Rosy KoIn distributed systems, there is a motto that says 'you are as slow as your slowest tasks'. In Python, thanks to the notorious Global Interpreter Lock (GIL), this issue is amplified: 'your slowest task will make every other task slower'. In this article, I'll walk you through the optimizations we made to scale a FastAPI service that now handles tens of thousands of requests per second, achieving a p99 latency under 10ms.Introduction OLX is a global online marketplace that enables users to buy and sell goods and services, primarily through classified ads. We have a clear vision: to create leading marketplace ecosystems enabled by tech, powered by trust, and loved by customers. Every month, we engage 45 million app users and support over 73 million active listings. To help users seamlessly navigate this vast inventory, we've integrated recommendation systems across multiple touchpoints in all our platforms. These recommendations are powered by the recommendations platform, which is responsible for delivering personalized suggestions across various contexts. Most, if not all, of these are served through a dedicated recommendations service. Over the past few months, we've built and scaled this system within the data team, successfully shifting the ownership from a shared backend service to a service fully owned by the team to gain greater autonomy and flexibility. The team decided to build it with Python, as it is the go-to language for the data and machine learning team and is the most widely used language within both the team and the broader domain. While Python allows for rapid development and prototyping, working at scale has surfaced several challenges and trade-offs. It hasn't been an easy journey, but it's one that's taught us a lot and significantly matured our infrastructure and processes. To Async or Not Async The service consumes data from various sources: ScyllaDB, DynamoD ## FIPS-ing the Un-FIPS-able: Apache Spark DevFeed: [FIPS-ing the Un-FIPS-able: Apache Spark]() Original publisher: [Read original article]() Published: 2025-04-17T00:00:00Z Content type: article Language: en Sources: [Chainguard: Unchained]() Topics: [Apache Spark](), [chainguard](), [container images](), [Cryptography](), [data-processing](), [distributed-systems](), [Data Infrastructure](), [Cybersecurity]() Tags: [apache](), [apache-spark](), [chainguard](), [chainguard-containers](), [cloud-native](), [compliance](), [container-images](), [cryptography](), [cybersecurity](), [data-infrastructure](), [fedramp](), [fips](), [fraud-detection](), [open-source](), [real-time-data-streaming](), [recommendation-systems](), [spark](), [spark-operator]() ### AI overview Chainguard announces FIPS-validated container images for Apache Spark and Spark Operator, built entirely from source. The article explains the demand for FIPS-compatible Spark in regulated environments and describes the effort to overcome incompatibilities between Spark and FIPS-approved cryptographic libraries. ### Source excerpt Chainguard now offers FIPS-validated container images for Apache Spark and Spark Operator. See how we did it. ## Train 400x faster Static Embedding Models with Sentence Transformers DevFeed: [Train 400x faster Static Embedding Models with Sentence Transformers]() Original publisher: [Read original article]() Author: Tom Aarsen Published: 2025-01-15T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [sentence-transformers](), [Embeddings](), [Natural language processing](), [On-device AI](), [Inference](), [datasets](), [Open Source](), [recommendation systems]() Tags: [benchmarks](), [community](), [dataset](), [edge-computing](), [embedded](), [embedding](), [embeddings](), [guide](), [inference](), [low-power](), [natural-language-processing](), [nlp](), [open-source](), [recommendation-systems](), [retrieval](), [sentence-transformers]() ### AI overview This Hugging Face blog post presents a method for training static embedding models that run 100x to 400x faster on CPU while retaining most of the quality of state-of-the-art models. It introduces released models for English retrieval and multilingual similarity, along with their training strategy, scripts, evaluation reports, and datasets. The approach supports on-device, in-browser, edge, low-power, and embedded use cases. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## An Open Course on LLMs, Led by Practitioners DevFeed: [An Open Course on LLMs, Led by Practitioners]() Original publisher: [Read original article]() Author: Hamel Husain Published: 2024-07-29T07:00:00Z Content type: release Language: en Sources: [Hamel Husain]() Topics: [LLMs](), [Tutorial](), [Fine-tuning](), [Retrieval Augmented Generation (RAG)](), [Prompt Engineering](), [Data Science](), [MLOps](), [recommendation systems]() Tags: [course](), [courses](), [data-science](), [fine-tuning](), [free](), [guide](), [llms](), [machine-learning](), [mlops](), [open](), [rag](), [recommendation-systems](), [tutorial]() ### AI overview The article announces Mastering LLMs, a free, open course of workshops and talks led by more than 25 industry practitioners. It covers applied topics including evaluations, retrieval-augmented generation, fine-tuning, application development, and prompt engineering, and is intended for technical professionals with basic LLM experience. ### Source excerpt Today, we are releasing Mastering LLMs, a set of workshops and talks from practitioners on topics like evals, retrieval-augmented-generation (RAG), fine-tuning and more. This course is unique because it is: Taught by 25+ industry veterans who are experts in information retrieval, machine learning, recommendation systems, MLOps and data science. We discuss how this prior art can be applied to LLMs to give you a meaningful advantage. Focused on applied topics that are relevant to people building AI products. Free and open to everyone . We have organized and annotated the talks from our popular paid course.1 This is a survey course for technical ICs (including engineers and data scientists) who have some experience with LLMs and need guidance on how to improve AI products. Speakers include Jeremy Howard, Sophia Yang, Simon Willison, JJ Allaire, Wing Lian, Mark Saroufim, Jane Xu, Jason Liu, Emmanuel Ameisen, Hailey Schoelkopf, Johno Whitaker, Zach Mueller, John Berryman, Ben Clavié, Abhishek Thakur, Kyle Corbitt, Ankur Goyal, Freddy Boulton, Jo Bergum, Eugene Yan, Shreya Shankar, Charles Frye, Hamel Husain, Dan Becker and more Getting The Most Value From The Course Prerequisites The course assumes basic familiarity with LLMs. If you do not have any experience, we recommend watching A Hacker's Guide to LLMs. We also recommend the tutorial Instruction Tuning llama2 if you are interested in fine-tuning 2. Navigating The Material The course has over 40 hours of content. To help you navigate this, we provide: Organization by subject area: evals, RAG, fine-tuning, building applications and prompt engineering. Chapter summaries: quickly peruse topics in each talk and skip ahead Notes, slides, and resources: these are resources used in the talk, as well as resources to learn more. Many times we have detailed notes as well! To get started, navigate to this page and explore topics that interest you. Feel free to skip sections that aren't relevant to you. We've organized the talks ## Location-Based Fashion Recommendations at Myntra DevFeed: [Location-Based Fashion Recommendations at Myntra]() Original publisher: [Read original article]() Author: Siddhartha Devapujula Published: 2024-04-22T11:25:37Z Content type: tutorial Language: en Sources: [Myntra]() Topics: [recommendation systems](), [Machine learning](), [recommendations]() Tags: [artificial-intelligence](), [ecommerce](), [fashion](), [location-based](), [machine-learning](), [personalisation](), [recommendation-system](), [recommendation-systems](), [search]() ### AI overview Myntra explains how machine-learning recommendation systems can use location as user side information to improve personalized fashion recommendations, including for cold-start users. ### Source excerpt Authored By Rohit Gupta & Siddhartha Devapujula Introduction Millions of users visit Myntra daily to upgrade their wardrobes and millions of items are listed on the platform at any given time. Users neither have the time nor the capability to scroll through this vast list of items. Even after applying category and attribute filters, usually the number of items is still in thousands. Hence it becomes critical that the top search results for any user are both relevant and personalized. Just like search, many other recommendation widgets across the platform face the same challenges. Fashion Diversity -- Every Region has its own Fashion Showing each user the best styles for them from a catalog of million plus products is where machine learning based recommendation systems come into play. From search results on google to your netflix home screen, recommendation systems are working in the background to get you the best results. It is impossible to imagine modern age internet experience without these systems. The uber goal of these models is to take the user features and the vast list of items as input ,and generate a small personalized list of items for each user. For these systems to work, we mainly use the user's historical activity on the platform. In this blog we will see how using other kinds of user details can also enhance the quality of recommendations. In the next sections, we dive into the details of recommendation systems and related techniques. We explain the motivation for a location based recommendation system and how we built one at Myntra. Later we discuss a few use cases at Myntra, results and potential future work. Basics of recommendation systems This is a very simple read about recommendation systems by Google -- Recommendations: What and Why? | Machine Learning | Google for Developers. Readers can skip if they are already aware of this. Traditional recommendation models focus on using the user's historical interactions on the platform to learn. This wor ## Building a Real-Time Recommendation System with Personalized Suggestions in Under 100ms DevFeed: [Building a Real-Time Recommendation System with Personalized Suggestions in Under 100ms]() Original publisher: [Read original article]() Author: Joe Karlsson Published: 2023-07-25T00:00:00Z Content type: tutorial Language: en Sources: [Tinybird]() Topics: [recommendation systems](), [real-time](), [systems]() Tags: [real-time](), [recommendation-system](), [recommendation-systems](), [scalable-analytics-architecture](), [systems]() ### AI overview A tutorial on building a real-time recommendation system that serves personalized suggestions in under 100 milliseconds. ### Source excerpt Real-time recommendation systems drive revenue but most are too slow. Here's how to serve personalized suggestions in under 100ms. ## 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! ## The Magic of Merlin: Shopify's New Machine Learning Platform DevFeed: [The Magic of Merlin: Shopify's New Machine Learning Platform]() Original publisher: [Read original article]() Author: Isaac Vidas Published: 2022-04-06T13:30:00Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [AI, ML & Data Engineering](), [Training AI Models](), [recommendation systems]() Tags: [architecture](), [inference](), [infrastructure](), [machine-learning](), [open-source](), [platform](), [recommendation-systems](), [shopify](), [training]() ### AI overview Shopify describes Merlin, a machine learning platform designed to streamline model workflows from training and testing through deployment, serving, and monitoring. The platform uses an open-source stack and initially focuses on training and batch inference. ### Source excerpt Merlin, Shopify's machine learning platform that can handle different (often conflicting) requirements, inputs, data types, dependencies, and integrations. ## Data Science at FMP: Dataset Recommendation DevFeed: [Data Science at FMP: Dataset Recommendation]() Original publisher: [Read original article]() Author: Tom Thurstan Published: 2021-12-23T00:00:00Z Content type: article Language: en Sources: [Findmypast]() Topics: [recommendation systems](), [Data Science](), [datasets](), [data]() Tags: [data](), [data-science](), [datasets](), [product](), [recommendation-systems](), [research]() ### AI overview Findmypast's data science team explores recommendation systems for helping users discover datasets relevant to their research. The article compares collaborative filtering and content-based filtering, explains why content-based filtering is unsuitable for this domain, and describes limitations caused by the lack of user ratings before introducing another approach. ### Source excerpt Problem Outline One of the problems we recently tackled in the data science team was how we could introduce our users to new datasets they may not be familiar with. We wanted these datasets to be personal to each user and relevant to their research areas. A recommendation system of some sort was the obvious way to solve this problem. Recommendation Systems Recommendation systems come with several possible methodologies. Two of the most widely known methodologies are Collaborative Filtering (which can be split into two branches, user-based or item-based) and Content-Based Filtering. User-based collaborative filtering recommendations are based on finding users with similar preferences and recommending items that similar users rated highly but have not been viewed by the target user. E.g., "Users who liked similar items to you also liked..." For item-based collaborative filtering, instead of finding similar users we find items that have been rated similarly by different users and recommended based on a user's previous preferences. E.g., "Users that liked this item also liked..." An important part of the data required for these techniques is ratings, so you can tell which items users liked. Another popular technique is Content-Based Filtering. This technique requires less information about each individual user and more about the items themselves. The recommendations are created by finding items that have similar characteristics, e.g., movies of the same genre, with the same actors, or directors. Then given a user's previous preferences you can recommend items similar to items they previously enjoyed. A broad and consistent range of metadata is needed for each of the items and again ratings are useful for this methodology. Within our domain at Findmypast a Content-Based Filtering system would not be appropriate. It is easy to see that some datasets which would share a lot of metadata would not necessarily make good recommendations. E.g., Kentucky Birth Records would most lik