1 ¤ ✞ ✝解 説 ✆ 情報推薦・情報フィルタリングのための ユーザプロファイリング技術 User Profiling Technique for Information Recommendation and Information Filtering 土方 嘉徳 大阪大学大学院基礎工学研
1 ¤ ✞ ✝解 説 ✆ 情報推薦・情報フィルタリングのための ユーザプロファイリング技術 User Profiling Technique for Information Recommendation and Information Filtering 土方 嘉徳 大阪大学大学院基礎工学研究科 Graduate School of Engineering Science, Osaka University Yoshinori Hijikata hijikata@sys.es.osaka-u.ac.jp, http://www.nishilab.sys.es.osaka-u.ac.jp/people/hijikata/index.html keywords: user profiling, information filtering, recommender system, relev
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https://consumervaluecreation.com/tag/recommendation-systemI bet you’ve come across a recommendation system in diverse ways, ranging from a commerce website where you buy goods,like Amazon;social network like Facebook; video/movies site like You-tube and Netflix. These site use the past record of movies,goods and friends to recommend new ones for you. In this post I will be introducing you briefly
Resource Timing Level 1 W3C Candidate Recommendation 30 March 2017 This version: https://www.w3.org/TR/2017/CR-resource-timing-1-20170330/ Latest published version: https://www.w3.org/TR/resource-timing-1/ Latest editor's draft: https://w3c.github.io/resource-timing/ Implementation report: https://w3c.github.io/test-results/resource-timing/all.html Previous version: https://www.w3.org/TR/2016/CR-r
GitHub - codelibs/elasticsearch-taste: Mahout Taste-based recommendation on Elasticsearch
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WCAG 2.0 Candidate Recommendation | アクセシビリティBlog | ミツエーリンクス
各所にて既報のとおり、Web Content Accessibility Guidelines (WCAG) 2.0のCandidate Recommendation (CR) が4月30日（日本時間5月1日）に公開されました。 参考：WCAG 2.0 勧告候補を公開 大きく内容に変更が見られた前回のSecond Last Working Draftとは違って、今回は比較的軽微な修正が主となっています。しかしながら、CRとしての公開において重要なポイントが、この文書のステータス（Status of this Document）という項に挙げられていますので、それを簡単にまとめておきたいと思います。 Exit Criteria 直訳すると「終了基準」となりますでしょうか。ここでは、CRのステータスを完了し、次のステップであるProposed Recommendation (PR) へと進むた
GitHub - takuti/flurs: :ocean: FluRS: A Python library for streaming recommendation algorithms
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Joonseok Lee, Mingxuan Sun, Guy Lebanon. PREA: Personalized Recommendation Algorithms Toolkit, Journal of Machine Learning Research (JMLR) 13:2699-2703, 2012. [BibTex] Joonseok Lee, Mingxuan Sun, Guy Lebanon. A Comparative Study of Collaborative Filtering Algorithms, ArXiv Report arXiv:1205.3193, 2012.
Opportunity model for e-commerce recommendation: right product; right time(SIGIR 2013) - 糞糞糞ネット弁慶
Opportunity model for e-commerce recommendation 概要 正しい商品を正しいタイミングで推薦したい． ノートパソコンを買った人が替えのバッテリーを購入しやすいという傾向があったとしても，それはバッテリーが駄目になる頃(例えば二年後とか)だろう．それをノートパソコンを買った直後にバッテリーを推薦するのは好ましくない． 解くべき問題としては，あるインターバル後に商品を購入する確率を推定する．に展開する．二項目は既存の推薦手法の出力をそのまま使えるので，問題はどうやってを推定するかということ． 購入時間の推定 ここで，生存時間分析の考えを持ってくる． 生存時間分析は文字通り，生存時間(観測開始から死亡までの時間)をモデリングする手法．もう少し広く言えば，観測開始からあるイベントがどれぐらいの相対時間で起こるかをモデリングする事が可能になる． 論文では，
Karen Schweitzer is a business school admissions consultant, curriculum developer, and education writer. She has been advising MBA applicants since 2005. Writing a recommendation letter for someone else is a huge responsibility, and getting everything just right plays an important role in that person's future. Looking at recommendation letter samples can provide inspiration and ideas for content a
自作アプリに使える「自由な地図」のススメ / Recommendation of "free map" for self-made apps
2019/07/13 OSC NAGOYAでのセミナー資料 https://www.ospn.jp/osc2019-nagoya/modules/eguide/event.php?eid=19 2019/08/03 OSC KYOTOでのセミナー資料（少しブラッシュアップ） https://w…
From stream to recommendation using apache beam with cloud pubsub and cloud dataflow
http://flink-forward.org/kb_sessions/apache-beam-a-unified-model-for-batch-and-streaming-data-processing/ Unbounded, unordered, global-scale datasets are increasingly common in day-to-day business, and consumers of these datasets have detailed requirements for latency, cost, and completeness. Apache Beam (incubating) defines a new data processing programming model that evolved from more than a dec
Whether it’s for a job or our own freelance work, getting a recommendation from someone is valuable and can help you nab a gig. Recommendations show the world we know our stuff. They tell others more about us, maybe a quality or skill we are a bit shy to disclose about ourselves. And they explain what it's like to work with you. Many social media sites, such as LinkedIn or BranchOut, offer the abi
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
In 2012, the HTML Working Group Chairs came up with a plan to progress HTML, aka "Plan 2014". The plan has several objectives: Produce a W3C Recommendation for HTML 5.0 before the end of 2014, as well as a W3C Recommendation for HTML 5.1 before the end of 2016; Use the Candidate Recommendation of HTML5, which started in December 2012, to focus the testing effort where it is appropriate; Use modula
As part of advancing HTML 5.0 to W3C Recommendation by 2014, the HTML Working Group Chairs proposed a plan today to work in parallel on stabilizing HTML 5.0 and developing the next generation of HTML features. The plan identifies, for the first time, how the Working Group will produce an HTML 5.1 Recommendation by 2016. The plan, not yet approved by the HTML Working Group, explains how the group a
Recommendation for compressing JPG files with ImageMagick
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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
GitHub - ryogrid/Kikker: web page recommendation web site system
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When it comes to recommendation systems, everybody's looking to increase accuracy: the Netflix Prize was awarded last July for an algorithm that improved the accuracy of the service's recommendation algorithm by 10 percent. However, computer scientists are finding a new metric to improve upon: recommendation diversity. In a paper that will be released by PNAS, a group of scientists are pushing the
AI Behind LinkedIn Recruiter Search and Recommendation Systems
Recommendations The AI Behind LinkedIn Recruiter search and recommendation systems Co-authors: Qi Guo, Sahin Cem Geyik, Cagri Ozcaglar, Ketan Thakkar, Nadeem Anjum, and Krishnaram Kenthapadi LinkedIn Talent Solutions serves as a marketplace for employers to reach out to potential candidates and for job seekers to find career opportunities. A key mechanism to help achieve these goals is the LinkedI
GitHub - cnclabs/smore: SMORe: Modularize Graph Embedding for Recommendation
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最近、消費税の軽減税率（主に「生活必需品」と思われるものに通常より低い税率を適用すること）の話題が盛り上がっている。「経済学者のほぼ全てが反対している」というから、実施する政府は何考えているのかわからないといった感想が聞かれるが、日本の政府は僕からすると何考えているのかわからない政策をたくさん実施しているので、軽減税率が導入されても特に驚かない。僕は逆に導入されなかったら驚くと思う。この手の、あまり考えなければよさそうに見える政策は常に実施されてきている。皮肉っぽく言うと、この手の政策を好む国民の代表が政府なのだから、実施されないほうが驚きだ。 では、なぜ、経済学者のほぼ全てが皆反対しているにも関わらず、実施されようとしているのか。それは、導入しないことの負の効果がわかりにくいからだと思う。軽減税率を実施した結果追加的に発生する財政負担が、他の増税、あるいは何らかの財政支出の削減という目に
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Beyond resumption of the Japanese Government's recommendation of the HPV vaccine - The Lancet Oncology
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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
We develop a Bayesian Poisson matrix factorization model for forming recommendations from sparse user behavior data. These data are large user/item matrices where each user has provided feedback on only a small subset of items, either explicitly (e.g., through star ratings) or implicitly (e.g., through views or purchases). In contrast to traditional matrix factorization approaches, Poisson factori
Media Source Extensions (W3C Candidate Recommendation 09 January 2014)
Media Source Extensions™ W3C Working Draft 04 July 2024 More details about this document This version: https://www.w3.org/TR/2024/WD-media-source-2-20240704/ 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
KKSlot777: Recommendation The Best Site #1 To Playing Games
RTP SLOT KKSLOT777 KkSlot777 KKSlot777 merupakan daftar rekomendasi situs terbaik #1 di Indonesia, Tersedia banyak jenis permainan games online berbagai genre terbaru.
Facebook 公式プラグインを使って、最近話題の Recommendation Bar を簡単設置する！ | thikasa note
ブログの記事を読み進めていると、右下からニョキって表示されるこれ、 Facebook の Recommendation Bar。 最近ちょっと流行ってます。 これを設置するには、コードを取得してそれを貼り付けて…というちょっとした手順が必要になるのですが、Facebook の公式プラグインを使うと簡単にできるので紹介します。 前準備：Facebook App ID 取得 コードを取得する方法にしろ、今回のプラグインを使う方法にしろ、いずれにしても Facebook App ID を取得しておく必要があります。 Facebook 系の機能（コメント設置とか）を使うのに今後も必要になるので、多少の手間はあるのですが、取得しておきましょう。 取得はこちらから。 Facebook開発者 こちらのページがわかりやすいと思います。 ※「App Namespace」も設定しておきましょう。 動画の解説も
I am currently participating in the Neo4j-Heroku Challenge. My entry is a -- as yet, unfinished -- beer rating and recommendation service called FrostyMug. All the major functionality is complete, except for the actual recommendations, which I am currently working on. I wanted to share some of my thoughts and methods for building the recommendation engine. My starting point for the recommendation
AT LAST YEAR’S consumer-electronics show in Las Vegas, Reed Hastings, the CEO of Netflix, set out an ambitious goal for serving his customers: “One day we hope to get so good at suggestions that we’re able to show you exactly the right film or TV show for your mood when you turn on Netflix.”
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
How to Build a Recommendation Engine on Spark
1. #AnalyticsStreet @joe_Caserta Building a Recommendation Engine on Spark Joe Caserta President, Caserta Concepts joe@casertaconcepts.com (914) 261-3648 @joe_Caserta 2. About Caserta Concepts • Technology services company with expertise in data analysis: • Big Data Solutions • Data Warehousing • Business Intelligence • Data Science & Analytics • Data on the Cloud • Data Interaction & Visualizatio
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,
Jemima Kiss: Web 3.0 is all about rank and recommendation
We had a real-life web-lebrity in the Guardian's offices last week: Martin Stiksel, one third of the brains behind the music recommendation site Last.fm. He's laidback and rather unassuming, but has the kind of enthusiasm and insight about his company that is often missing in serial executives. He'd probably say that's because he always did it for the music not the money, but seems to have ended u
CSS Color Module Level 3 (W3C Proposed Recommendation 28 October 2010)
W3C Proposed Recommendation 28 October 2010 This version: http://www.w3.org/TR/2010/PR-css3-color-20101028 Latest version: http://www.w3.org/TR/css3-color Previous version: http://www.w3.org/TR/2008/WD-css3-color-20080721 Editors: Tantek Çelik (invited expert, and before at Microsoft Corporation) <tantek@cs.stanford.edu> Chris Lilley (W3C) <chris@w3.org> L. David Baron (Mozilla Corporation) <dbaro
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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Foursquare Experimenting With Recommendation Engine
Launch of the 1st consultation on the implementation of the 2021 Recommendation on Open Science
Introduction To Recommendation system In Javascript
Resource Timing (W3C Candidate Recommendation 25 March 2014)
PREA (Personalized Recommendation Algorithms Toolkit)
Good Recommendation Letters Share a Few Key Qualities
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HTML5: On Our Way to Recommendation
Getting HTML5 to Recommendation in 2014
Recommendation algorithm wants to show you something new
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Decentralized Identifiers (DIDs) v1.0 is a W3C Recommendation
Scalable Recommendation with Poisson Factorization
Item-Based Collaborative Filtering Recommendation Algorithms
Similarity-based Recommendation Engines
How to devise the perfect recommendation algorithm
Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models
Taxonomy Discovery for Personalized Recommendation