Facebook Drops Google Chrome Recommendation, Replaces It With Opera
You are here: Home » Google » Facebook Drops Google Chrome Recommendation, Replaces It With Opera More fuel to the rumor’s fire. It looks like Facebook management decided not to bother with the Google Chrome anymore as their latest “unsupported web browsers” page has since then removed the search giant’s web browser. If you haven’t been living under a rock for the last few days, chances are, you’v
Disclaimer Generally speaking I'm a little wary about HTML form libraries now. If you use something from a mega-framework, you invariably have to bring in the whole mega-framework as your dependency. Many sub-components of many mega-frameworks claim to not depend on the framework but let's not kid ourselves. If you don't use one, there are at least a dozen form libraries that I know of out there w
GitHub - apache/brpc: brpc is an Industrial-grade RPC framework using C++ Language, which is often used in high performance system such as Search, Storage, Machine learning, Advertisement, Recommendation etc. "brpc" means "better RPC".
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Selecting Keywords for Content Based Recommendation - CIKM(2010) - Qiita
Selecting Keywords for Content Based Recommendation - CIKM(2010)機械学習推薦システム 1. Introduction 新しいアイテムの推薦を考えるためにコンテンツベースの推薦を扱う. テレビ番組・映画の推薦をメタデータとあらすじまたはプロットを用いて行う 本論文のContributionはメタデータの比較を行ったこと. classicalなアプローチのように最も高い性能をもつキーワードセットを得ようとしたのではなく, 人手で抽出したキーワードと自動で抽出したキーワードを比較し, 提案手法が人手で抽出したものより推薦に適していることを示した. さらにそれ以上キーワードを加えても推薦の精度を高めることが出来ないことも示した. 2. Related Works 3. Content-Based Recommendation 2つのデ
"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
DOM Parsing and Serialization (W3C Candidate Recommendation 17 June 2014)
DOM Parsing and Serialization DOMParser, XMLSerializer, innerHTML, and similar APIs W3C Working Draft 17 May 2016 This version: http://www.w3.org/TR/2016/WD-DOM-Parsing-20160517/ Latest published version: http://www.w3.org/TR/DOM-Parsing/ Latest editor's draft: https://w3c.github.io/DOM-Parsing/ Test suite: http://w3c-test.org/domparsing/ Previous version: http://www.w3.org/TR/2014/CR-DOM-Parsing-
Survey of Japanese mothers of daughters eligible for human papillomavirus vaccination on attitudes about media reports of adverse events and the suspension of governmental recommendation for vaccination - PubMed
The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site. The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.
GitHub - tkyk/cakephp-cicindela: CakePHP Library to access Cicindela recommendation engine.
CakePHP Cicindela Library --------------------------------------------- Cicindela is an open source recommendation engine. http://code.google.com/p/cicindela2/ Requirements -------------------------- PHP 5, CakePHP 1.2.x, and Cicindela 2 Configurations -------------------------- This library consists of a DataSource and a Behavior. * database.php <?php class DATABASE_CONFIG { var $cicindela = arra
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
A new WHO guideline recommends adults and children reduce their daily intake of free sugars to less than 10% of their total energy intake. A further reduction to below 5% or roughly 25 grams (6 teaspoons) per day would provide additional health benefits. Guideline on sugars intake for adult and children Free sugars refer to monosaccharides (such as glucose, fructose) and disaccharides (such as suc
GitHub - NVIDIA-Merlin/Transformers4Rec: Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and works with PyTorch.
Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and can work with PyTorch. The library works as a bridge between natural language processing (NLP) and recommender systems (RecSys) by integrating with one of the most popular NLP frameworks, Hugging Face Transformers (HF). Transformers4Rec makes state-of-the-art transformer architectures available
Factorizing Personalized Markov Chains for Next-Basket Recommendation (WWW 2010) 読んだ - 糞糞糞ネット弁慶
Factorizing Personalized Markov Chains for Next-Basket Recommendation(pdf) 概要 「各ユーザが次に何を買うか」というタスクに対してマルコフ連鎖ベースの予測モデルを作る． ユーザごとの遷移確率を計算するにはスパースなので行列分解と組み合わせてその問題を解消する． 問題 ユーザの回分の購買履歴があるとして，回目で何を買うかをあてる．また，一度の購買では複数の商品を購入している(これをバスケットと表現する)． 手法 まず1購買に複数商品があるので，真面目にやるとそれぞれのアイテムのあるなしを1/0で表すと次元のマルコフ連鎖を考えなければならない． なので，「商品が前回のバスケットに入っている時，次のバスケットに入っている確率」を考える． 遷移確率は数え上げで計算ができるし，ユーザごとの履歴に絞ればpersonalizeも可
In most cryptographic functions, the key length is an important security parameter. Both academic and private organizations provide recommendations and mathematical formulas to approximate the minimum key size requirement for security. Despite the availability of these publications, choosing an appropriate key size to protect your system from attacks remains a headache as you need to read and unde
Sparkを触る機会が増えてきてるので、知識の棚卸しを兼ねてMLlib使ってレコメンデーションシステムを実装してみました。SparkSamit2014などMLlibのチュートリアル的に色々使われているSparkのMovie Recommendationですが、edXのIntroduction to Big Data with Apache Sparが内容的にも良さそうだったので、題材にしながら実装しました。本講座はSpark 1.3.1での実装ですが少し古すぎるので、1.6.1で使える機能は使う形でコード変えてます。 おおまかな手順 ①データの準備 元データを訓練、評価、テストデータにそれぞれ分割 ②評価数500以上の映画の中から平均評価点が高いものを表示 ③協調フィルタリングの実装 ④訓練データに自分をuserID"0"として加え、好きな映画を評価 ⑤自分の評価をもとに、アルゴリズムに映
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
The Web Platform Working Group has published a W3C Recommendation of the HTML 5.2 specification that would obsolete the HTML 5.1 Recommendation. The HTML 5.2 specification defines the 5th major version, second minor revision of the core language of the World Wide Web: the Hypertext Markup Language (HTML). In this version, new features continue to be introduced to help Web application authors, new
Online Casino Recommendation Ranking! Thorough Comparison of Online Casinos and Internet Casinos If you are looking through this article, you are probably interested in the following 1. looking for a recommended online casino 2. want to understand the important points to consider when choosing an online casino. To meet these needs, the author, who has played at online casinos all his life, has cre
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding (WSDM 2018) 読んだ - 糞糞糞ネット弁慶
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding (pdf) A Simple Convolutional Generative Network for Next Item Recommendation (WSDM 2019) を読もうとしたところ引用されていたのでまずはこちらから読む．WSDM 2019 の方は dilated 1d conv + residual unit という感じで WaveNet に非常によく似た形なのであまり読むモチベーションが上がらない．余談ですがこの構造を "A Simple but Hard-to-Beat Baseline" と名付ける著者らが理解できません． 問題はユーザ における閲覧などによって得られる 個の item の系列 を入力とし
Using Graph Theory to Build a Simple Recommendation Engine in JavaScript
Working at Storefront, we’re always excited about new ways we can keep our users engaged. Recommendations or suggestions are a fantastic way for a platform to encourage users to stick around and keep browsing. The problem is, recommendations can be tricky. How do we know what to recommend to our customers? Is it similar item descriptions? Colors? Locations? It could be anything, and a linear combi
Software Top 10 movie recommendation engines If you don't know what to watch Friday night, look no further than this list of the top movie recommendation engines on the Web. They all offer something different. There are dozens of movie recommendation engines on the Web. Some require little or no input before they give you titles, while others want to find out exactly what your interests are. I've
PLOS、著者のデータリポジトリ選択を支援するPLOS Data Repository Recommendation Guideを公開 | 科学技術情報プラットフォーム
PLOSは7月2日、データポリシーの更新に伴い、著者のデータリポジトリ選択を支援するため、PLOS Data Repository Recommendation Guideを公開した。本ガイドでは、PLOSが調査した各コミュニティーで認識され信用されている既存のリポジトリをリスト化している。 PLOSは、著者のリポジトリ選択において、オープンデータポリシーの遵守を規定してはいないが、CC BYライセンス以上の制約がないライセンスポリシーのリポジトリで論文が公開され、論文の根拠となるデータがパブリックアクセス可能であることを要求している。 [ニュースソース] PLOS Recommended Data Repositories - The PLOS ONE Community Blog 2015/07/02
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
CSS Containment Module Level 1 (W3C Candidate Recommendation, 8 August 2017)
CSS Containment Module Level 1 W3C Recommendation, 25 June 2024 More details about this document This version: https://www.w3.org/TR/2024/REC-css-contain-1-20240625/ Latest published version: https://www.w3.org/TR/css-contain-1/ Editor's Draft: https://drafts.csswg.org/css-contain-1/ Previous Versions: https://www.w3.org/TR/2022/REC-css-contain-1-20221025/ History: https://www.w3.org/standards/his
What is the Alternating Least Squares method in recommendation systems? And why does this algorithm work (intuition behind this)?
Answer (1 of 6): Before discussing ALS, let’s briefly discuss the least squares problem (in particular, regularised least squares). Let’s consider a feature matrix X \in \mathbb{R}^{m \times d} and target value y \in \mathbb{R}^{m \times 1} then regularised least squares optimises \arg\!\min \l...
(PDF) Beyond Personalization: Research Directions in Multistakeholder Recommendation
Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other important aspects of recommendation outcomes. Properti
Thousands of engineers write the code to create our apps, which serve billions of people worldwide. This is no trivial task—our services have grown so diverse and complex that the codebase contains millions of lines of code that intersect with a wide variety of different systems, from messaging to image rendering. To simplify and speed the process of writing code that will make an impact on so man
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
Trust-Based Recommendation Systems: an Axiomatic Approachを読んだメモ - NO!と言えるようになりたい
WWW 2008(http://www2008.org/)で発表された論文である，"Trust-Based Recommendation Systems: an Axiomatic Approach"を読んだメモです．この論文は公理に基づいて，Recommendationシステムの解析をしようというモノですが，いまいち分からなかったので，詳細は省きます^^; ですが，この論文中で紹介されていた，Personalizing PageRankが面白かったので，メモっておきます． 普通のPageRankでは，ネットワーク（有向グラフ）の繋がり全てを再帰的に走査し，どのノードが重要かをランクづけます．しかしながら，Personalizing PageRankでは，あるノードにとって，他のノードはどれぐらい重要かと言うことを，ランダムウォークを用いて求めます． ランダムウォークのアルゴリズムは以下の
Flickr Tag Recommendation based on Collective Knowledge (application/pdf オブジェクト)
The WWW2008 Conference site has been archived at: thewebconf.org This notice is provided as a courtesy in memory of RFC 2068 and HTTP Status Code 402.
Media Source Extensions (W3C Candidate Recommendation 09 January 2014)
Media Source Extensions™ W3C Editor's Draft 01 April 2024 More details about this document This version: https://w3c.github.io/media-source/ Latest published version: https://www.w3.org/TR/media-source-2/ Latest editor's draft:https://w3c.github.io/media-source/ History: https://www.w3.org/standards/history/media-source-2/ Commit history Latest Recommendation:https://www.w3.org/TR/2016/REC-media-s
How to Ask Your Professor for a Recommendation Letter Via Email
This article was co-authored by Alexander Ruiz, M.Ed. and by wikiHow staff writer, Glenn Carreau. Alexander Ruiz is an Educational Consultant and the Educational Director of Link Educational Institute, a tutoring business based in Claremont, California that provides customizable educational plans, subject and test prep tutoring, and college application consulting. With over a decade and a half of
Context-Aware Music Recommendation Based on Latent Topic Sequential Patterns (Recsys 2012)
Context-Aware Music Recommendation Based on Latent Topic Sequential Patterns Negar Hariri DePaul University School of Computing Chicago, IL 60604, USA nhariri@cs.depaul.edu Bamshad Mobasher DePaul University School of Computing Chicago, IL 60604, USA mobasher@cs.depaul.edu Robin Burke DePaul University School of Computing Chicago, IL 60604, USA burke@cs.depaul.edu ABSTRACT Contextual factors can
Campaign / Recommendation-Accommodation Plan ｜ Toyoko Inn-Hotel / Business Hotel Reservation
The Toyoko Inn hotel chain welcomes your reservations for hotel and business hotel accommodations.
Next: Introduction Item-based Collaborative Filtering Recommendation Algorithms Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl {sarwar, karypis, konstan, riedl}@cs.umn.edu GroupLens Research Group/Army HPC Research Center Department of Computer Science and Engineering University of Minnesota, Minneapolis, MN 55455 Copyright is held by the author/owner(s). WWW10, May 1-5, 2001, Hong
増井雄一郎の「wri.pe」を事例に学ぶ、自作サービスの作り方〜サービスデザイン編 先生：増井 雄一郎schoowebcampus
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
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Recommendation for python form validation library
米IDPF、電子書籍ファイルフォーマット規格「EPUB 3」がついにFinal Recommendation版になったことを発表 | HON.jp News Blog
WHO guideline : sugar consumption recommendation
Keylength - Cryptographic Key Length Recommendation
Spark MLlibの協調フィルタリングを活用したMovie Recommendation - Qiita
On YouTube’s recommendation system
HTML 5.2 is now a W3C Recommendation
Online Casino Recommendation Ranking! Thorough Comparison of Online Casinos and Internet Casinos
Top 10 movie recommendation engines - CNET
/blog/2014/03/security-advisory-2953095-recommendation-to-stay-protected-and-for-detections/
Aroma: Using ML for code recommendation
WCAG 2.2 Recommendation (勧告) | Accessible & Usable
PLOS、データリポジトリの選択を支援するための“Data Repository Recommendation Guide”を公開
Recommendation concerning the International Standardization of Statistics Relating to Book Production and Periodicals
Item-based Collaborative Filtering Recommendation Algorithms
Recommendation for iruby #tqrk08