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  • GraphQL Client Architecture Recommendation 社外版 | メルカリエンジニアリング

    この記事は、Merpay Advent Calendar 2022 の15日目の記事です。 こんにちは。メルペイのvvakameです。 最近、社内向けにGraphQL Client Architecture Recommendationというドキュメントを書きました。社内のiOS/Android、そしてバックエンドのエンジニア向けにGraphQLをやるならこの辺りの条件を満たしておかないと恩恵を感じられなくなっちゃうかもよ、と伝えるためのものです。嬉しいことに、今までに100名弱の人たちがこのドキュメントを閲覧してくれたようです。 これをAdvent Calendarで公開するために、ちょっと調整したものがこの社外版です。 すでにGraphQLをやっているけどあまり便利じゃないな…なんでだろ?とか、これから導入したいんだけど何を気をつけるべきかな…と考える時の材料にしてください。 併せて、

      GraphQL Client Architecture Recommendation 社外版 | メルカリエンジニアリング
    • Twitter's Recommendation Algorithm

      Twitter aims to deliver you the best of what’s happening in the world right now. This requires a recommendation algorithm to distill the roughly 500 million Tweets posted daily down to a handful of top Tweets that ultimately show up on your device’s For You timeline. This blog is an introduction to how the algorithm selects Tweets for your timeline. Our recommendation system is composed of many in

        Twitter's Recommendation Algorithm
      • GitHub - twitter/the-algorithm: Source code for Twitter's Recommendation Algorithm

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          GitHub - twitter/the-algorithm: Source code for Twitter's Recommendation Algorithm
        • A Survey on Large Language Models for Recommendation

          Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) and have recently gained significant attention in the domain of Recommendation Systems (RS). These models, trained on massive amounts of data using self-supervised learning, have demonstrated remarkable success in learning universal representations and have the potential to enhance various

          • Generative Recommendation : LLMを活用した推薦システム | Wantedly Engineer Blog

            この記事はWantedly Advent Calendar 2023 兼 情報検索・検索技術 Advent Calendar 2023の3日目の記事です。 ウォンテッドリーでデータサイエンティストをしている角川(@nogawanogawa)です。ウォンテッドリーのデータサイエンスチームは、9/18〜9/23にシンガポールにて開催されたRecSys2023に聴講参加しました。 RecSys 2023, the seventeenth conference in this series, will be held in Singapore. It will bring together researchers and practitioners from academia and industry to present their latest results and identify new

              Generative Recommendation : LLMを活用した推薦システム | Wantedly Engineer Blog
            • Replace Create React App recommendation with Vite by t3dotgg · Pull Request #5487 · reactjs/react.dev

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                Replace Create React App recommendation with Vite by t3dotgg · Pull Request #5487 · reactjs/react.dev
              • The journey to build an explainable AI-driven recommendation system to help scale sales efficiency across LinkedIn

                Recommendations The journey to build an explainable AI-driven recommendation system to help scale sales efficiency across LinkedIn Authored byJilei Yang Staff Software Engineer, Machine Learning at LinkedIn | PhD in Statistics April 6, 2022 Co-authors: Jilei Yang, Parvez Ahammmad, Fangfang Tan, Rodrigo Aramayo, Suvendu Jena, Jessica Li At LinkedIn, we have the opportunity to work with many differe

                  The journey to build an explainable AI-driven recommendation system to help scale sales efficiency across LinkedIn
                • Building a recommendation engine inside Postgres with Python and Pandas | Crunchy Data Blog

                  I'm a big fan of data in general. Data can tell you a lot about what users are doing and can help you gain all sorts of insights. One such aspect is in making recommendations based on past history or others that have made similar choices. In fact, years ago I wrote a small app to see if I could recommend wines based on how other ones were rated. It was a small app that I shared among just a handfu

                    Building a recommendation engine inside Postgres with Python and Pandas | Crunchy Data Blog
                  • Potential for cervical cancer incidence and death resulting from Japan’s current policy of prolonged suspension of its governmental recommendation of the HPV vaccine - Scientific Reports

                    In May of 2018, the Director-General of the World Health Organization (WHO) announced a global call-to-action towards the elimination of cervical cancer1. In January of 2019, a global strategy for doing just that was announced at the 144th Session of the WHO Executive Board2. They listed the known barriers to the worldwide elimination of cervical cancer, such as a Human Papilloma Virus (HPV) vacci

                      Potential for cervical cancer incidence and death resulting from Japan’s current policy of prolonged suspension of its governmental recommendation of the HPV vaccine - Scientific Reports
                    • World Wide Web Consortium (W3C) brings a new language to the Web as WebAssembly becomes a W3C Recommendation

                      Following HTML, CSS and JavaScript, WebAssembly becomes the fourth language for the Web which allows code to run in the browser Read testimonials from W3C Members https://www.w3.org/ — 5 December 2019 — The World Wide Web Consortium (W3C) announced today that the WebAssembly Core Specification is now an official web standard, launching a powerful new language for the Web. WebAssembly is a safe, po

                        World Wide Web Consortium (W3C) brings a new language to the Web as WebAssembly becomes a W3C Recommendation
                      • Web Real-Time Communications (WebRTC) transforms the communications landscape; becomes a World Wide Web Consortium (W3C) Recommendation and multiple Internet Engineering Task Force (IETF) standards

                        WebRTC enables rich, interactive, live voice and video communications anywhere on the Web, boosting global interconnection Read testimonials from W3C Members. https://www.w3.org/ and https://www.ietf.org/ — 26 January 2021 — The World Wide Web Consortium (W3C) and the Internet Engineering Task Force (IETF) announced today that Web Real-Time Communications (WebRTC), which powers myriad services, is

                          Web Real-Time Communications (WebRTC) transforms the communications landscape; becomes a World Wide Web Consortium (W3C) Recommendation and multiple Internet Engineering Task Force (IETF) standards
                        • 2019-12-10のJS: Firefox 71.0、WebAssembly W3C Recommendation、Nullish Coalescing & Optional Chaining(ES2020)

                          JSer.info #465 - Firefox 71.0がリリースされました。 Firefox 71.0, See All New Features, Updates and Fixes Firefox 71 for Developers - Mozilla | MDN Firefox 71 サイト互換性情報 | Firefox サイト互換性情報 Firefox 71.0では開発者ツールの改善が多く含まれています。 コンソールパネルではmulti-line modeをサポート、デバッガーパネルではInline variable previewをサポート、ネットワークパネルではRequest Blockingがサポートされています。 また、素のWebSocketsやSocket.IOなどのメッセージをデバッグできるWeb Sockets Inspectorがデフォルトで有効化されています。

                            2019-12-10のJS: Firefox 71.0、WebAssembly W3C Recommendation、Nullish Coalescing & Optional Chaining(ES2020)
                          • Lessons Learnt From Consolidating ML Models in a Large Scale Recommendation System

                            by Roger Menezes, Rahul Jha, Gary Yeh, and Sudarshan Lamkhede In this blog post, we share system design lessons from consolidating several related machine learning models for large-scale search and recommendation systems at Netflix into a single unified model. Given different recommendation use cases, many recommendation systems treat each use-case as a separate machine-learning task and train a b

                              Lessons Learnt From Consolidating ML Models in a Large Scale Recommendation System
                            • 論文紹介 Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems

                              社内論文読み会の資料です Mehrotra, Rishabh, et al. "Towards a fair marketplace: Counterfactual evaluation of the trade-off between relevance, fairness & satisfac…

                                論文紹介 Towards a Fair Marketplace: Counterfactual Evaluation of the trade-off between Relevance, Fairness & Satisfaction in Recommendation Systems
                              • FacebookがDLRM(Deep-Learning Recommendation Model)をオープンソース公開

                                Spring BootによるAPIバックエンド構築実践ガイド 第2版 何千人もの開発者が、InfoQのミニブック「Practical Guide to Building an API Back End with Spring Boot」から、Spring Bootを使ったREST API構築の基礎を学んだ。この本では、出版時に新しくリリースされたバージョンである Spring Boot 2 を使用している。しかし、Spring Boot3が最近リリースされ、重要な変...

                                  FacebookがDLRM(Deep-Learning Recommendation Model)をオープンソース公開
                                • Transformers in music recommendation

                                  Philosophy We strive to create an environment conducive to many different types of research across many different time scales and levels of risk. Learn more about our Philosophy Learn more

                                  • "Item Recommendation from Implicit Feedback"の紹介 | | AI tech studio

                                    AILab Creative Researchチームの富樫です。 このブログでは先月末にarxivに投稿された“Item Recommendation from Implicit Feedback”[1]という論文を軸に紹介しつつ、 周辺分野の話題について議論したいと思います。 この論文はitem推薦というタスクにおける手法の各種パラダイムの概観をコンパクトに解説した教科書的内容になっています。 著者はBayesian Personalized Ranking (BPR)[2]を開発したGoogle Research所属のSteffen Rendle氏であり、 長年この分野を開拓してきた権威の一人です。 元論文の内容は元論文を読めばわかることですし、 蛇足かもしれませんが、最近の研究との関連性や議論、個人的な感想などを示すことで、このブログが元論文に対する補足資料のようになることを目指した

                                      "Item Recommendation from Implicit Feedback"の紹介 | | AI tech studio
                                    • GitHub - pytorch/torchrec: Pytorch domain library for recommendation systems

                                      Parallelism primitives that enable easy authoring of large, performant multi-device/multi-node models using hybrid data-parallelism/model-parallelism. The TorchRec sharder can shard embedding tables with different sharding strategies including data-parallel, table-wise, row-wise, table-wise-row-wise, column-wise, table-wise-column-wise sharding. The TorchRec planner can automatically generate opti

                                        GitHub - pytorch/torchrec: Pytorch domain library for recommendation systems
                                      • GitHub - NVIDIA-Merlin/Transformers4Rec: Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and works with PyTorch.

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                                          GitHub - NVIDIA-Merlin/Transformers4Rec: Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and works with PyTorch.
                                        • On YouTube’s recommendation system

                                          Inside YouTube On YouTube’s recommendation system By Cristos Goodrow, VP of Engineering At YouTube Sep 15, 2021 – minute read When YouTube’s recommendations are at their best, they connect billions of people around the world to content that uniquely inspires, teaches, and entertains. For me, that means diving into lectures exploring the ethical questions facing technology today or watching highlig

                                            On YouTube’s recommendation system
                                          • リーダブルコミットのすゝめ / Recommendation of Readable Commit

                                            2021/05/29 PHPカンファレンス沖縄 2021 でトークした際に使用したスライドです

                                              リーダブルコミットのすゝめ / Recommendation of Readable Commit
                                            • WCAG 2.2 Recommendation (勧告) | Accessible & Usable

                                              公開日 : 2023年10月6日 (2024年3月2日 更新) カテゴリー : アクセシビリティ W3C の WCAG (Web Content Accessibility Guidelines) の新バージョンである WCAG 2.2 が、2023年10月5日に正式な Recommendation (勧告) になりました。 Web Content Accessibility Guidelines (WCAG) 2.2 - W3C Recommendation 05 October 2023 またこれに併せて、W3C の WAI (Web Accessibility Initiative) より以下の関連文書が公開されています。 WCAG 2.2 Understanding Docs WCAG 2.2 Techniques これまでも当サイトでは WCAG 2.2 策定の道のりをウォッチし

                                                WCAG 2.2 Recommendation (勧告) | Accessible & Usable
                                              • Campaign / Recommendation-Accommodation Plan | Toyoko Inn-Hotel / Business Hotel Reservation

                                                The Toyoko Inn hotel chain welcomes your reservations for hotel and business hotel accommodations.

                                                • CDC on Twitter: "#DYK? CDC’s recommendation on wearing a cloth face covering may help protect the most vulnerable from #COVID19. Wat… https://t.co/GwYdqi1vad"

                                                  #DYK? CDC’s recommendation on wearing a cloth face covering may help protect the most vulnerable from #COVID19. Wat… https://t.co/GwYdqi1vad

                                                    CDC on Twitter: "#DYK? CDC’s recommendation on wearing a cloth face covering may help protect the most vulnerable from #COVID19. Wat… https://t.co/GwYdqi1vad"
                                                  • Deep Transfer Learning for Search and Recommendation The WEB Conference 2020 Tutorial

                                                    Training data sparsity is a common problem for many real-world applications in Search and Recommendation domains. Even for applications with a lot of training data, in the cold-start scenario we usually do not get enough labeled data. Transfer Learning is a promising approach to address this problem by bridging the generalization gap from the related applications into the new one. With the increas

                                                      Deep Transfer Learning for Search and Recommendation The WEB Conference 2020 Tutorial
                                                    • Principal Machine Learning Engineer - Candidate Recommendation - External Careers

                                                      Our mission. As the world’s number 1 job site, our mission is to help people get jobs. We need talented, passionate people working together to make this happen. We are looking to grow our teams with people who share our energy and enthusiasm for creating the best experience for job seekers. We are a rapidly growing and highly-capable Engineering organization building the most popular job site on t

                                                        Principal Machine Learning Engineer - Candidate Recommendation - External Careers
                                                      • WebRTC transforms the communications landscape as it becomes a W3C Recommendation and IETF standards

                                                        About Introduction to the IETF Introduction to the IETF Participants Mission Principles The work Structure of the IETF Internet Engineering Steering Group Internet Architecture Board Internet Research Task Force Liaisons Nominating Committee IETF Trust Legal policies and requests Legal request procedures Legal requests Other policies Note Well IETF/IRTF/IAB Privacy Statement Anti-harassment policy

                                                        • Introducing TorchRec, a library for modern production recommendation systems

                                                          by Meta AI - Donny Greenberg, Colin Taylor, Dmytro Ivchenko, Xing Liu, Anirudh Sudarshan We are excited to announce TorchRec, a PyTorch domain library for Recommendation Systems. This new library provides common sparsity and parallelism primitives, enabling researchers to build state-of-the-art personalization models and deploy them in production. How did we get here? Recommendation Systems (RecSy

                                                          • Launch of the 1st consultation on the implementation of the 2021 Recommendation on Open Science

                                                              Launch of the 1st consultation on the implementation of the 2021 Recommendation on Open Science
                                                            • Director's Decision on DID 1.0 Proposed Recommendation Formal Objections

                                                              Director's Decision on DID 1.0 Proposed Recommendation Formal Objections Status: Final The W3C Member review of the Decentralized Identifier (DID) 1.0 Proposed Recommendation concluded with Formal Objections from three organizations. Google: DID-core is only useful with the use of "DID methods", which need their own specifications. ... It's impossible to review the impact of the core DID specifica

                                                              • Cloud TPU VMs with Ranking & Recommendation are generally available | Google Cloud Blog

                                                                Earlier last year, Cloud TPU VMs on Google Cloud were introduced to make it easier to use the TPU hardware by providing direct access to TPU host machines. Today, we are excited to announce the general availability (GA) of TPU VMs. With Cloud TPU VMs you can work interactively on the same hosts where the physical TPU hardware is attached. Our rapidly growing TPU user community has enthusiastically

                                                                  Cloud TPU VMs with Ranking & Recommendation are generally available | Google Cloud Blog
                                                                • Decentralized Identifiers (DIDs) v1.0 is a W3C Recommendation

                                                                  The Decentralized Identifier Working Group has published Decentralized Identifiers (DIDs) v1.0 as a W3C Recommendation. This document defines Decentralized identifiers (DIDs), a new type of identifier that enables verifiable, decentralized digital identity. A DID identifies any subject (e.g., a person, organization, thing, data model, abstract entity, etc.) that the controller of the DID decides t

                                                                    Decentralized Identifiers (DIDs) v1.0 is a W3C Recommendation
                                                                  • Transformers4Rec: A flexible library for Sequential and Session-based recommendation

                                                                    Recommender systems help users to find relevant content, products, media and much more in online services. They also help such services to connect their long-tailed (unpopular) items to the right people, to keep their users engaged and increase conversion. Traditional recommendation algorithms, e.g. collaborative filtering, usually ignore the temporal dynamics and the sequence of interactions when

                                                                      Transformers4Rec: A flexible library for Sequential and Session-based recommendation
                                                                    • How LotteON built a personalized recommendation system using Amazon SageMaker and MLOps | Amazon Web Services

                                                                      AWS Machine Learning Blog How LotteON built a personalized recommendation system using Amazon SageMaker and MLOps This post is co-written with HyeKyung Yang, Jieun Lim, and SeungBum Shim from LotteON. LotteON aims to be a platform that not only sells products, but also provides a personalized recommendation experience tailored to your preferred lifestyle. LotteON operates various specialty stores,

                                                                        How LotteON built a personalized recommendation system using Amazon SageMaker and MLOps | Amazon Web Services
                                                                      • Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models

                                                                        Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand in its data-centers. In this paper we discuss the SW/HW co-designed solution for high-performance distributed training of large-scale DLRMs. We introduce a high-performance scalable software stack based on PyTorch and pa

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