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Recommendation for dummy
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Buhwan Jeong
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추천시스템 개요 및 분류 등.
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Recommandé
Netflix, Amazons Recommendation is nothing but Collaborative filtering algorithm. It is of two types : 1) User to User Based 2) Item to Item Based Detail algorithm Described in the slide.
Recommendation system Using Collaborative Filtering
Recommendation system Using Collaborative Filtering
Mrinal Kanti Ghosh
Overview of the Recommender system or recommendation system. RFM Concepts in brief. Collaborative Filtering in Item and User based. Content-based Recommendation also described.Product Association Recommender System. Stereotype Recommendation described with advantage and limitations.Customer Lifetime. Recommender System Analysis and Solving Cycle.
Recommender system
Recommender system
Nilotpal Pramanik
16.03.18(금) 양주 일영앞뜰펜션
16-1학기 ITS 10기 오리엔테이션
16-1학기 ITS 10기 오리엔테이션
고려대학교 정보기술경영학회 : ITS
ITS 4차 메인 세미나_알고리즘(배은정, 김용겸, 김성수, 정민영, 유재현) 왓챠(Watcha) 알고리즘 분석(15.11.06) 고려대학교 정보기술경영학회 : ITS Web: http://itsociety.co.kr/ Mail: president@itsociety.co.kr
[4차]왓챠 알고리즘 분석(151106)
[4차]왓챠 알고리즘 분석(151106)
고려대학교 정보기술경영학회 : ITS
모교 후배들에게 강연한 발표 자료를 공유합니다. 예비 프로그래머분들에게 도움이 됐으면 좋겠습니다.
프로그래머를 꿈꾸는 학부 후배들에게
프로그래머를 꿈꾸는 학부 후배들에게
Matthew (정재화)
데이터 과학자의 실체 The Reality of Data Scientist 전체 분석 과정에서 대부분은 데이터를 모으고 가공하는데 소요한다. 그리고 애플리케이션에 데이터를 적용하기 위해서는 테스팅이 가장 중요하다. 인간공학 전공자들을 대상으로 준비한 발표자료라서 '데이터 수집 및 클렌징'보다는 '테스트 (온라인 테스트)'에 초점을 두고 자료를 만들었습니다.
Life of a data scientist (pub)
Life of a data scientist (pub)
Buhwan Jeong
Josh Elman, Partner, Greylock Hear how the former product guy from Linkedin and Twitter grew their users from 10s of thousands to over 200 million users. He'll share his framework for measuring growth.
3 Growth Hacks: The Secrets to Driving Massive User Growth | Josh Elman, Grey...
3 Growth Hacks: The Secrets to Driving Massive User Growth | Josh Elman, Grey...
Dealmaker Media
ITS 4차 메인 세미나_알고리즘 A조(오진영, 조한빈, 성지영, 오정민, 김영균) 페이스북 알고리즘 분석(15.10.30) 고려대학교 정보기술경영학회 : ITS Web: http://itsociety.co.kr/ Mail: president@itsociety.co.kr
[4차]페이스북 알고리즘 분석(151106)
[4차]페이스북 알고리즘 분석(151106)
고려대학교 정보기술경영학회 : ITS
Recommandé
Netflix, Amazons Recommendation is nothing but Collaborative filtering algorithm. It is of two types : 1) User to User Based 2) Item to Item Based Detail algorithm Described in the slide.
Recommendation system Using Collaborative Filtering
Recommendation system Using Collaborative Filtering
Mrinal Kanti Ghosh
Overview of the Recommender system or recommendation system. RFM Concepts in brief. Collaborative Filtering in Item and User based. Content-based Recommendation also described.Product Association Recommender System. Stereotype Recommendation described with advantage and limitations.Customer Lifetime. Recommender System Analysis and Solving Cycle.
Recommender system
Recommender system
Nilotpal Pramanik
16.03.18(금) 양주 일영앞뜰펜션
16-1학기 ITS 10기 오리엔테이션
16-1학기 ITS 10기 오리엔테이션
고려대학교 정보기술경영학회 : ITS
ITS 4차 메인 세미나_알고리즘(배은정, 김용겸, 김성수, 정민영, 유재현) 왓챠(Watcha) 알고리즘 분석(15.11.06) 고려대학교 정보기술경영학회 : ITS Web: http://itsociety.co.kr/ Mail: president@itsociety.co.kr
[4차]왓챠 알고리즘 분석(151106)
[4차]왓챠 알고리즘 분석(151106)
고려대학교 정보기술경영학회 : ITS
모교 후배들에게 강연한 발표 자료를 공유합니다. 예비 프로그래머분들에게 도움이 됐으면 좋겠습니다.
프로그래머를 꿈꾸는 학부 후배들에게
프로그래머를 꿈꾸는 학부 후배들에게
Matthew (정재화)
데이터 과학자의 실체 The Reality of Data Scientist 전체 분석 과정에서 대부분은 데이터를 모으고 가공하는데 소요한다. 그리고 애플리케이션에 데이터를 적용하기 위해서는 테스팅이 가장 중요하다. 인간공학 전공자들을 대상으로 준비한 발표자료라서 '데이터 수집 및 클렌징'보다는 '테스트 (온라인 테스트)'에 초점을 두고 자료를 만들었습니다.
Life of a data scientist (pub)
Life of a data scientist (pub)
Buhwan Jeong
Josh Elman, Partner, Greylock Hear how the former product guy from Linkedin and Twitter grew their users from 10s of thousands to over 200 million users. He'll share his framework for measuring growth.
3 Growth Hacks: The Secrets to Driving Massive User Growth | Josh Elman, Grey...
3 Growth Hacks: The Secrets to Driving Massive User Growth | Josh Elman, Grey...
Dealmaker Media
ITS 4차 메인 세미나_알고리즘 A조(오진영, 조한빈, 성지영, 오정민, 김영균) 페이스북 알고리즘 분석(15.10.30) 고려대학교 정보기술경영학회 : ITS Web: http://itsociety.co.kr/ Mail: president@itsociety.co.kr
[4차]페이스북 알고리즘 분석(151106)
[4차]페이스북 알고리즘 분석(151106)
고려대학교 정보기술경영학회 : ITS
ITS 4차 메인 세미나_알고리즘(권성현, 김민희, 이상윤, 송서하, 정현정 | 최진호) 구글 알고리즘 분석(15.11.06) 고려대학교 정보기술경영학회 : ITS Web: http://itsociety.co.kr/ Mail: president@itsociety.co.kr
[4차]구글 알고리즘 분석(151106)
[4차]구글 알고리즘 분석(151106)
고려대학교 정보기술경영학회 : ITS
뉴럴 랭귀지 모델 Word2vec
From A Neural Probalistic Language Model to Word2vec
From A Neural Probalistic Language Model to Word2vec
Jungkyu Lee
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컨텐츠 기반 A/B 테스트 구현 사례
컨텐츠 기반 A/B 테스트 구현 사례
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SK플래닛 @tech 판교세미나에서 발표한 Deep learning 기반T map POI 추천 기술 개발 사례입니다.
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Today's teachers need to evolve with their students and society. It is no longer enough to master the basics--students need and want 21st century skills.
A or B
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Randy Rodgers
The term sketchnoting describes a style of visual note-taking recently gaining popularity among conference attendees. Contrary to popular belief, you do not have to be an artist to sketchnote and to take advantage of a different type of learning and making content connections beyond conference keynotes . Sketchnoting is helping make your thinking visible and shareable as you are reading a professional book, watching a movie clip, reading an educational blog post or article or listening to a lecture of conference keynote. This workshop is for educators who want to hone their abilities to listen more intently, summarize and organize their notes in a visual way and learn how to do this with their students. NO artistic talent required. Want to work with me? Contact me via http://www.globallyconnectedlearning.com
Sketchnoting FOR Learning
Sketchnoting FOR Learning
Silvia Rosenthal Tolisano
프론트엔드와 실무 경험담을 위주로 설명한 프레젠테이션입니다.
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추천아 놀자 4회 방송 영화 분류하기 사용 스냅 자료
추놀 4회 영화 분류하기
추놀 4회 영화 분류하기
choi kyumin
유사도 측정(우리아기는 누구와 더 닮았는가?) cosine vs euclidean
추놀 3회 유사도 측정(우리아기는 누구와 더 닮았는가?)
추놀 3회 유사도 측정(우리아기는 누구와 더 닮았는가?)
choi kyumin
ITS 4차 메인 세미나_알고리즘(김범수, 김요섭, 최민철, 함주현 | 최한울) 넷플릭스 알고리즘 분석(15.11.06) 고려대학교 정보기술경영학회 : ITS Web: http://itsociety.co.kr/ Mail: president@itsociety.co.kr
[4차]넷플릭스 알고리즘 분석(151106)
[4차]넷플릭스 알고리즘 분석(151106)
고려대학교 정보기술경영학회 : ITS
플랫폼데이2013 workflow기반 실시간 스트리밍데이터 수집 및 분석 플랫폼 발표자료
플랫폼데이2013 workflow기반 실시간 스트리밍데이터 수집 및 분석 플랫폼 발표자료
choi kyumin
YOU are the fire that refines your content.. Allow yourself to shine through, and trust, connection, and sharing will follow.
The Anatomy of Great Content (and the fire that refines it)
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PyCon APAC2016 에서 발표한 자료임
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choi kyumin
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Haystacks slides
Haystacks slides
Ted Sullivan
Recommendation systems, also known as recommendation engines, are a type of information system whose purpose is to suggest, or recommend items or actions to users. The recommendations may consist of: -> retail items (movies, books, etc.) or -> actions, such as following other users in a social network. It can be said that, Recommendation engines are nothing but an automated form of a “shop counter guy”. You ask him for a product. Not only he shows that product, but also the related ones which you could buy. They are well trained in cross selling and up selling. So, does our recommendation engines.
Recommendation Systems Basics
Recommendation Systems Basics
Jarin Tasnim Khan
Social Recommender Systems Tutorial - WWW 2011
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idoguy
Guest Lecture for MSc Information Retrieval course, October 20th, 2010, University of Twente.
Twente ir-course 20-10-2010
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Arjen de Vries
Contenu connexe
En vedette
ITS 4차 메인 세미나_알고리즘(권성현, 김민희, 이상윤, 송서하, 정현정 | 최진호) 구글 알고리즘 분석(15.11.06) 고려대학교 정보기술경영학회 : ITS Web: http://itsociety.co.kr/ Mail: president@itsociety.co.kr
[4차]구글 알고리즘 분석(151106)
[4차]구글 알고리즘 분석(151106)
고려대학교 정보기술경영학회 : ITS
뉴럴 랭귀지 모델 Word2vec
From A Neural Probalistic Language Model to Word2vec
From A Neural Probalistic Language Model to Word2vec
Jungkyu Lee
하둡을 잘 모르는 사람을 위한 하둡 HDFS 훑어보기 자료입니다.
하둡 HDFS 훑어보기
하둡 HDFS 훑어보기
beom kyun choi
컨텐츠 기반 A/B 테스트 구현 사례 @tech 세미나 SK플래닛
컨텐츠 기반 A/B 테스트 구현 사례
컨텐츠 기반 A/B 테스트 구현 사례
Lee Ji Eun
SK플래닛 @tech 판교세미나에서 발표한 Deep learning 기반T map POI 추천 기술 개발 사례입니다.
Deep learning 기반TmapPOI 추천기술개발사례
Deep learning 기반TmapPOI 추천기술개발사례
Lee Ji Eun
Today's teachers need to evolve with their students and society. It is no longer enough to master the basics--students need and want 21st century skills.
A or B
A or B
Randy Rodgers
The term sketchnoting describes a style of visual note-taking recently gaining popularity among conference attendees. Contrary to popular belief, you do not have to be an artist to sketchnote and to take advantage of a different type of learning and making content connections beyond conference keynotes . Sketchnoting is helping make your thinking visible and shareable as you are reading a professional book, watching a movie clip, reading an educational blog post or article or listening to a lecture of conference keynote. This workshop is for educators who want to hone their abilities to listen more intently, summarize and organize their notes in a visual way and learn how to do this with their students. NO artistic talent required. Want to work with me? Contact me via http://www.globallyconnectedlearning.com
Sketchnoting FOR Learning
Sketchnoting FOR Learning
Silvia Rosenthal Tolisano
프론트엔드와 실무 경험담을 위주로 설명한 프레젠테이션입니다.
웹 Front-End 실무 이야기
웹 Front-End 실무 이야기
JinKwon Lee
추천아 놀자 4회 방송 영화 분류하기 사용 스냅 자료
추놀 4회 영화 분류하기
추놀 4회 영화 분류하기
choi kyumin
유사도 측정(우리아기는 누구와 더 닮았는가?) cosine vs euclidean
추놀 3회 유사도 측정(우리아기는 누구와 더 닮았는가?)
추놀 3회 유사도 측정(우리아기는 누구와 더 닮았는가?)
choi kyumin
ITS 4차 메인 세미나_알고리즘(김범수, 김요섭, 최민철, 함주현 | 최한울) 넷플릭스 알고리즘 분석(15.11.06) 고려대학교 정보기술경영학회 : ITS Web: http://itsociety.co.kr/ Mail: president@itsociety.co.kr
[4차]넷플릭스 알고리즘 분석(151106)
[4차]넷플릭스 알고리즘 분석(151106)
고려대학교 정보기술경영학회 : ITS
플랫폼데이2013 workflow기반 실시간 스트리밍데이터 수집 및 분석 플랫폼 발표자료
플랫폼데이2013 workflow기반 실시간 스트리밍데이터 수집 및 분석 플랫폼 발표자료
choi kyumin
YOU are the fire that refines your content.. Allow yourself to shine through, and trust, connection, and sharing will follow.
The Anatomy of Great Content (and the fire that refines it)
The Anatomy of Great Content (and the fire that refines it)
Elan Morgan
PyCon APAC2016 에서 발표한 자료임
2016 PyCon APAC - 너의 사진은 내가 지난 과거에 한일을 알고 있다.
2016 PyCon APAC - 너의 사진은 내가 지난 과거에 한일을 알고 있다.
choi kyumin
En vedette
(14)
[4차]구글 알고리즘 분석(151106)
[4차]구글 알고리즘 분석(151106)
From A Neural Probalistic Language Model to Word2vec
From A Neural Probalistic Language Model to Word2vec
하둡 HDFS 훑어보기
하둡 HDFS 훑어보기
컨텐츠 기반 A/B 테스트 구현 사례
컨텐츠 기반 A/B 테스트 구현 사례
Deep learning 기반TmapPOI 추천기술개발사례
Deep learning 기반TmapPOI 추천기술개발사례
A or B
A or B
Sketchnoting FOR Learning
Sketchnoting FOR Learning
웹 Front-End 실무 이야기
웹 Front-End 실무 이야기
추놀 4회 영화 분류하기
추놀 4회 영화 분류하기
추놀 3회 유사도 측정(우리아기는 누구와 더 닮았는가?)
추놀 3회 유사도 측정(우리아기는 누구와 더 닮았는가?)
[4차]넷플릭스 알고리즘 분석(151106)
[4차]넷플릭스 알고리즘 분석(151106)
플랫폼데이2013 workflow기반 실시간 스트리밍데이터 수집 및 분석 플랫폼 발표자료
플랫폼데이2013 workflow기반 실시간 스트리밍데이터 수집 및 분석 플랫폼 발표자료
The Anatomy of Great Content (and the fire that refines it)
The Anatomy of Great Content (and the fire that refines it)
2016 PyCon APAC - 너의 사진은 내가 지난 과거에 한일을 알고 있다.
2016 PyCon APAC - 너의 사진은 내가 지난 과거에 한일을 알고 있다.
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Collaborative filtering
Aravindharamanan S
his talk will feature some of my recent research into the alternative uses for Solr facets and facet metadata. I will develop the idea that facets can be used to discover similarities between items and attributes in a search index, and show some interesting applications of this idea. A common takeaway is that using facets and facet metadata in non-conventional ways enables the semantic context of a query to be automatically tuned. This has important implications for user-centric and semantically focused relevance.
Haystacks slides
Haystacks slides
Ted Sullivan
Recommendation systems, also known as recommendation engines, are a type of information system whose purpose is to suggest, or recommend items or actions to users. The recommendations may consist of: -> retail items (movies, books, etc.) or -> actions, such as following other users in a social network. It can be said that, Recommendation engines are nothing but an automated form of a “shop counter guy”. You ask him for a product. Not only he shows that product, but also the related ones which you could buy. They are well trained in cross selling and up selling. So, does our recommendation engines.
Recommendation Systems Basics
Recommendation Systems Basics
Jarin Tasnim Khan
Social Recommender Systems Tutorial - WWW 2011
Social Recommender Systems Tutorial - WWW 2011
idoguy
Guest Lecture for MSc Information Retrieval course, October 20th, 2010, University of Twente.
Twente ir-course 20-10-2010
Twente ir-course 20-10-2010
Arjen de Vries
LOGIC SYSTEMS 1st Floor, Reddy Comlex, Opp. SPencers, Near Satyam Theatre, Ameerpet, Hyderabad 9533694296,9703109334 logicsystemsprojects@gmail.com www.logicsystems.org.in
typicality-based collaborative filtering recommendation
typicality-based collaborative filtering recommendation
swathi78
Overview of recommender system
Overview of recommender system
Overview of recommender system
Stanley Wang
Sentiment analysis, also known as opinion mining, is a field of computer science that focuses on automatically identifying the opinions and feelings expressed in text, audio and video. It aims to determine whether a document expresses a subjective view (positive, negative, or neutral) or presents objective facts. Sentiment analysis involves determining the sentiment expressed by a writer in a document. The objective of the opinion-mining field is to conduct subjectivity analysis, indicating whether a document is subjective or objective. Subjectivity implies the presence of sentiment, while objectivity signifies content devoid of sentiment. Currently, an abundance of information about a specific product is available, with a single product often garnering hundreds of reviews across various webpages. Numerous websites, such as imdb.com, amazon.com, idlebrain.com, among others, aggregate user information and expert opinions to publish reviews. Experts meticulously analyze reviews, extract opinions, and generate ratings related to the dataset provided by the requesting agencies. However, handling the vast amount of data is a labor-intensive task for experts. The continuously growing volume of web data poses challenges in extracting precise opinions from content. Hence, there is a need to design a system that can efficiently perform these tasks with human-like accuracy. In this research work, the propose approach enough capable of handling and analyzing large amounts of reviews. The reviews considered of analyzing are pre-analyzed with existing algorithms and further processed through the approach proposed in the present research work. The working capacity of the proposed approach extracts sentiment from the available content (dataset) and determines polarity degree using sentiment polarity and degree management. It also measures sentiment degrees based on user-provided target document features. The outcome is a summary comprising highly sentiment-related sentences, providing valuable insights to the users. The goal is to streamline sentiment analysis processes and enhance accuracy in a manner that aligns with human-like comprehension.
opinionminingkavitahyunduk00-110407113230-phpapp01.ppt
opinionminingkavitahyunduk00-110407113230-phpapp01.ppt
ssuser059331
Sentiment analysis, also known as opinion mining, is a field of computer science that focuses on automatically identifying the opinions and feelings expressed in text, audio and video. It aims to determine whether a document expresses a subjective view (positive, negative, or neutral) or presents objective facts. Sentiment analysis involves determining the sentiment expressed by a writer in a document. The objective of the opinion-mining field is to conduct subjectivity analysis, indicating whether a document is subjective or objective. Subjectivity implies the presence of sentiment, while objectivity signifies content devoid of sentiment. Currently, an abundance of information about a specific product is available, with a single product often garnering hundreds of reviews across various webpages. Numerous websites, such as imdb.com, amazon.com, idlebrain.com, among others, aggregate user information and expert opinions to publish reviews. Experts meticulously analyze reviews, extract opinions, and generate ratings related to the dataset provided by the requesting agencies. However, handling the vast amount of data is a labor-intensive task for experts. The continuously growing volume of web data poses challenges in extracting precise opinions from content. Hence, there is a need to design a system that can efficiently perform these tasks with human-like accuracy. In this research work, the propose approach enough capable of handling and analyzing large amounts of reviews. The reviews considered of analyzing are pre-analyzed with existing algorithms and further processed through the approach proposed in the present research work. The working capacity of the proposed approach extracts sentiment from the available content (dataset) and determines polarity degree using sentiment polarity and degree management. It also measures sentiment degrees based on user-provided target document features. The outcome is a summary comprising highly sentiment-related sentences, providing valuable insights to the users. The goal is to streamline sentiment analysis processes and enhance accuracy in a manner that aligns with human-like comprehension.
opinionminingkavitahyunduk00-110407113230-phpapp01.ppt
opinionminingkavitahyunduk00-110407113230-phpapp01.ppt
ssuser059331
THIS IS ABOUT AN MOVIE RATING,USING SENTIMENTAL KEYWORDS
COMMENT POLARITY MOVIE RATING SYSTEM-1.pptx
COMMENT POLARITY MOVIE RATING SYSTEM-1.pptx
5088manoj
Digital Trails Dave King 1 5 10 Part 2 D3
Digital Trails Dave King 1 5 10 Part 2 D3
Dave King
Introduction to Recommender Systems
Lecture Notes on Recommender System Introduction
Lecture Notes on Recommender System Introduction
PerumalPitchandi
Slides of my PhD defense - Exploiting distributional semantics for content-based and context-aware recommendation
PhD defense - Exploiting distributional semantics for content-based and conte...
PhD defense - Exploiting distributional semantics for content-based and conte...
Victor Codina
Similaire à Recommendation for dummy
(16)
Collaborative filtering hyoungtae cho
Collaborative filtering hyoungtae cho
ADM6274 - Final (NEHA)
ADM6274 - Final (NEHA)
Online feedback correlation using clustering
Online feedback correlation using clustering
Collaborative filtering
Collaborative filtering
Haystacks slides
Haystacks slides
Recommendation Systems Basics
Recommendation Systems Basics
Social Recommender Systems Tutorial - WWW 2011
Social Recommender Systems Tutorial - WWW 2011
Twente ir-course 20-10-2010
Twente ir-course 20-10-2010
typicality-based collaborative filtering recommendation
typicality-based collaborative filtering recommendation
Overview of recommender system
Overview of recommender system
opinionminingkavitahyunduk00-110407113230-phpapp01.ppt
opinionminingkavitahyunduk00-110407113230-phpapp01.ppt
opinionminingkavitahyunduk00-110407113230-phpapp01.ppt
opinionminingkavitahyunduk00-110407113230-phpapp01.ppt
COMMENT POLARITY MOVIE RATING SYSTEM-1.pptx
COMMENT POLARITY MOVIE RATING SYSTEM-1.pptx
Digital Trails Dave King 1 5 10 Part 2 D3
Digital Trails Dave King 1 5 10 Part 2 D3
Lecture Notes on Recommender System Introduction
Lecture Notes on Recommender System Introduction
PhD defense - Exploiting distributional semantics for content-based and conte...
PhD defense - Exploiting distributional semantics for content-based and conte...
Plus de Buhwan Jeong
딥러닝 Deep Learning에 관해서 팀에 공유했던 발표자료입니다.
Deep learning - Conceptual understanding and applications
Deep learning - Conceptual understanding and applications
Buhwan Jeong
2013.11에 포항공과대학교 산업경영공학과 학부1년생들을 대상으로 한 산업공학입문 시간에 발표할 내용입니다. 아직 두달의 시간이 남아서 내용의 일부가 바뀔 수도 있으나 전체 맥락/스토리가 정해져서 미리 공유합니다. 내용에 대한 부연설명은 블로그에 다시 올리겠습니다. (일부 사진은 구글검색을 통해서 삽입한 것입니다. 법적 문제가 발생할 수 있으니 참고 바랍니다.)
포스트 테일러 시대에 살아남기
포스트 테일러 시대에 살아남기
Buhwan Jeong
파일 사이즈 때문에 몇몇 페이지는 생략함.
Unexperienced pasts
Unexperienced pasts
Buhwan Jeong
A short minority report about search experience and keyword management
Minority Report about Search Experience & Keyword Management
Minority Report about Search Experience & Keyword Management
Buhwan Jeong
2011년도 다음개발자컨퍼런스에서 발표한 가이드쿼리 및 관련검색어에 대한 발표자료입니다.
DDC2011 - Association
DDC2011 - Association
Buhwan Jeong
울산대학교 (산공과 학부생)와 포항공과대학교 (산경과 대학원생)들을 대상으로 한 발표자료를 축약한 것임. 일부개인정보를 포함한 경우나 전체 흐름에 부가적인 것들을 제외시켰고, 애니메이션으로된 사진을 작은 사진으로 줄임.
Internet Trends (C*), Search & Social
Internet Trends (C*), Search & Social
Buhwan Jeong
Plus de Buhwan Jeong
(6)
Deep learning - Conceptual understanding and applications
Deep learning - Conceptual understanding and applications
포스트 테일러 시대에 살아남기
포스트 테일러 시대에 살아남기
Unexperienced pasts
Unexperienced pasts
Minority Report about Search Experience & Keyword Management
Minority Report about Search Experience & Keyword Management
DDC2011 - Association
DDC2011 - Association
Internet Trends (C*), Search & Social
Internet Trends (C*), Search & Social
Dernier
Dubai, known for its towering skyscrapers, luxurious lifestyle, and relentless pursuit of innovation, often finds itself in the global spotlight. However, amidst the glitz and glamour, the emirate faces its own set of challenges, including the occasional threat of flooding. In recent years, Dubai has experienced sporadic but significant floods, disrupting normalcy and posing unique challenges to its infrastructure. Among the critical nodes in this bustling metropolis is the Dubai International Airport, a vital hub connecting the world. This article delves into the intersection of Dubai flood events and the resilience demonstrated by the Dubai International Airport in the face of such challenges.
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Orbitshub
Architecting Cloud Native Applications
Architecting Cloud Native Applications
WSO2
Six common myths about ontology engineering, knowledge graphs, and knowledge representation.
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontology
johnbeverley2021
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Deepika Singh
The CNIC Information System is a comprehensive database managed by the National Database and Registration Authority (NADRA) of Pakistan. It serves as the primary source of identification for Pakistani citizens and residents, containing vital information such as name, date of birth, address, and biometric data.
CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
danishmna97
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Keynote 2: APIs in 2030: The Risk of Technological Sleepwalk Paolo Malinverno, Growth Advisor - The Business of Technology Apidays New York 2024: The API Economy in the AI Era (April 30 & May 1, 2024) ------ Check out our conferences at https://www.apidays.global/ Do you want to sponsor or talk at one of our conferences? https://apidays.typeform.com/to/ILJeAaV8 Learn more on APIscene, the global media made by the community for the community: https://www.apiscene.io Explore the API ecosystem with the API Landscape: https://apilandscape.apiscene.io/
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
apidays
Workshop Build With AI - Google Developers Group Rio Verde
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
Sandro Moreira
In this talk, we are going to cover the use-case of food image generation at Delivery Hero, its impact and the challenges. In particular, we will present our image scoring solution for filtering out inappropriate images and elaborate on the models we are using.
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
Zilliz
Uncertainty, Acting under uncertainty, Basic probability notation, Bayes’ Rule,
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
Khushali Kathiriya
💥 You’re lucky! We’ve found two different (lead) developers that are willing to share their valuable lessons learned about using UiPath Document Understanding! Based on recent implementations in appealing use cases at Partou and SPIE. Don’t expect fancy videos or slide decks, but real and practical experiences that will help you with your own implementations. 📕 Topics that will be addressed: • Training the ML-model by humans: do or don't? • Rule-based versus AI extractors • Tips for finding use cases • How to start 👨🏫👨💻 Speakers: o Dion Morskieft, RPA Product Owner @Partou o Jack Klein-Schiphorst, Automation Developer @Tacstone Technology
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
UiPathCommunity
Dubai, often portrayed as a shimmering oasis in the desert, faces its own set of challenges, including the occasional threat of flooding. Despite its reputation for opulence and modernity, the emirate is not immune to the forces of nature. In recent years, Dubai has experienced sporadic but significant floods, testing the resilience of its infrastructure and communities. Among the critical lifelines in this bustling metropolis is the Dubai International Airport, a bustling hub that connects the city to the world. This article explores the intersection of Dubai flood events and the resilience demonstrated by the Dubai International Airport in the face of such challenges.
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Orbitshub
Join our latest Connector Corner webinar to discover how UiPath Integration Service revolutionizes API-centric automation in a 'Quote to Cash' process—and how that automation empowers businesses to accelerate revenue generation. A comprehensive demo will explore connecting systems, GenAI, and people, through powerful pre-built connectors designed to speed process cycle times. Speakers: James Dickson, Senior Software Engineer Charlie Greenberg, Host, Product Marketing Manager
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
DianaGray10
In this keynote, Asanka Abeysinghe, CTO,WSO2 will explore the shift towards platformless technology ecosystems and their importance in driving digital adaptability and innovation. We will discuss strategies for leveraging decentralized architectures and integrating diverse technologies, with a focus on building resilient, flexible, and future-ready IT infrastructures. We will also highlight WSO2's roadmap, emphasizing our commitment to supporting this transformative journey with our evolving product suite.
Platformless Horizons for Digital Adaptability
Platformless Horizons for Digital Adaptability
WSO2
This reviewer is for the second quarter of Empowerment Technology / ICT in Grade 11
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
MadyBayot
This Slide deck talk about how FHIR is being used in Ayushman Bharat Digital Mission (ABDM). It introduces the readers to ABDM and also to FHIR Documents paradigm. This is part of FHIR India community Basics learning initiative.
Introduction to use of FHIR Documents in ABDM
Introduction to use of FHIR Documents in ABDM
Kumar Satyam
Discover the innovative features and strategic vision that keep WSO2 an industry leader. Explore the exciting 2024 roadmap of WSO2 API management, showcasing innovations, unified APIM/APK control plane, natural language API interaction, and cloud native agility. Discover how open source solutions, microservices architecture, and cloud native technologies unlock seamless API management in today's dynamic landscapes. Leave with a clear blueprint to revolutionize your API journey and achieve industry success!
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2
Three things you will take away from the session: • How to run an effective tenant-to-tenant migration • Best practices for before, during, and after migration • Tips for using migration as a springboard to prepare for Copilot in Microsoft 365 Main ideas: Migration Overview: The presentation covers the current reality of cross-tenant migrations, the triggers, phases, best practices, and benefits of a successful tenant migration Considerations: When considering a migration, it is important to consider the migration scope, performance, customization, flexibility, user-friendly interface, automation, monitoring, support, training, scalability, data integrity, data security, cost, and licensing structure Next Wave: The next wave of change includes the launch of Copilot, which requires businesses to be prepared for upcoming changes related to Copilot and the cloud, and to consolidate data and tighten governance ShareGate: ShareGate can help with pre-migration analysis, configurable migration tool, and automated, end-user driven collaborative governance
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
sammart93
Terragrunt, Terraspace, Terramate, terra... whatever. What is wrong with Terraform so people keep on creating wrappers and solutions around it? How OpenTofu will affect this dynamic? In this presentation, we will look into the fundamental driving forces behind a zoo of wrappers. Moreover, we are going to put together a wrapper ourselves so you can make an educated decision if you need one.
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
Andrey Devyatkin
Presented by Mike Hicks
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
ThousandEyes
Dernier
(20)
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Architecting Cloud Native Applications
Architecting Cloud Native Applications
Six Myths about Ontologies: The Basics of Formal Ontology
Six Myths about Ontologies: The Basics of Formal Ontology
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CNIC Information System with Pakdata Cf In Pakistan
CNIC Information System with Pakdata Cf In Pakistan
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Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
DEV meet-up UiPath Document Understanding May 7 2024 Amsterdam
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Platformless Horizons for Digital Adaptability
Platformless Horizons for Digital Adaptability
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
EMPOWERMENT TECHNOLOGY GRADE 11 QUARTER 2 REVIEWER
Introduction to use of FHIR Documents in ABDM
Introduction to use of FHIR Documents in ABDM
WSO2's API Vision: Unifying Control, Empowering Developers
WSO2's API Vision: Unifying Control, Empowering Developers
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
Recommendation for dummy
1.
A Brief Introduction
to ! Recommendation ! (Fallacies & Understanding) Jeong, Buhwan (Ph.D)
2.
3.
4.
X Data-driven Automated Personalized
5.
Everything, but Nothing
6.
For anyone For one
in a group For a person For an item
7.
Explicit Rating vs
Implicit Feedback
8.
Content-based Filtering (CBF) Collaborative
Filtering (CF)
9.
Model-based CF Memory-based CF Matrix
Factorization (MF)
10.
User-orientation vs Item-orientation I Us Me I Is
11.
Similarity Measures ! Many common
items between users Many common users between items
12.
Similar Items? Similar Users? MxN Co-occurrence,
Set theory, Distance, Correlation, Cosine, Kernel
13.
Hybrid (Ensemble) Explicit Rating Collaborative
Filtering User Orientation Implicit Feedback + Content-based Filtering Item Orientation
14.
Search Recommendation Goal Retrieval Discovery Query Keyword User or Item Result Documents Items BM25 CBF PageRank CF Ranking Recency,
Quality, Filtering, Diversification
15.
ShoppingHow ! Item- & memory-based
CF with implicit feedback Hybrid with CBF using category, mall, brand info.
16.
Curse of Dimensionality
17.
n axa n axN MxN m = Mxa m
18.
MF = SVD
= LSA/LSI
19.
Let’s play music
20.
How to Evaluate?
21.
Accuracy vs User
Satisfaction
22.
Fast Iteration >>
Good Algorithm
23.
Post Analysis &
Review
24.
New Perspective ! Netflix’s micro
tagging/genre Amazon’s anticipatory shipping
25.
Cold-start Data sparsity Dimensional complexity Coverage Serendipity
& Diversity Explainability
26.
PR = P
+ M + R + F
27.
Just do it.
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