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# RecSys-Tech-Blog-Article
- 추천 시스템 관련 자료를 모으고 있습니다.
- 추천 시스템을 만드는데 필요한 자료라면 무엇이든 추가할 예정입니다.
- 추가할 자료가 있다면 PR을 남겨주세요.

## Airbnb
- [2022, Graph Machine Learning at Airbnb](https://medium.com/airbnb-engineering/graph-machine-learning-at-airbnb-f868d65f36ee)
- [2017, Introducing Pixie, an advanced graph-based recommendation system](https://medium.com/pinterest-engineering/introducing-pixie-an-advanced-graph-based-recommendation-system-e7b4229b664b)

## HYPERCONNECT
- [2023, 실시간 추천 시스템을 위한 Feature Store 구현기](https://deview.kr/2023/sessions/536)
- [2022, 이벤트 기반의 라이브 스트리밍 추천 시스템 운용하기](https://hyperconnect.github.io/2022/01/24/event-driven-recsys.html)
- [2021, 비용 효율적인 Click-Through Rate Prediction 모델로 하쿠나 라이브 추천시스템 구축하기](https://hyperconnect.github.io/2021/04/26/hakuna-recsys-gb.html)

## KAKAO
- [2022, Explainable Recommender System in 카카오웹툰](https://if.kakao.com/2022/session/9)
- [2022, Sequential Recommendation System 카카오 서비스 적용기](https://if.kakao.com/2022/session/8)
- [2021, 카카오 AI추천 : 협업 필터링 모델 선택 시의 기준에 대하여](https://tech.kakao.com/2021/10/18/collaborative-filtering/)
- [2021, 카카오 AI추천 : 토픽 모델링과 MAB를 이용한 카카오 개인화 추천](https://tech.kakao.com/2021/06/25/kakao-ai-recommendation-01/)
- [2021, 카카오 AI추천 : 카카오페이지와 멜론으로 살펴보는 카카오 연관 추천](https://tech.kakao.com/2021/05/20/kakao-ai-recommendation/)
- [2021, 카카오 AI 추천을 소개합니다.](https://tech.kakao.com/2021/03/11/kakao-ai/)
- [2020, 멜론에서 음악 추천을 어떻게 할까?](https://brunch.co.kr/@kakao-it/342)
- [2019, 브런치 추천의 힘에 대한 6가지 기술](https://brunch.co.kr/@kakao-it/333)
- [2018, 열독률 적용한 카카오 추천 알고리즘, 그 결과는?](https://brunch.co.kr/@kakao-it/212)
- [2017, [카카오AI리포트]내 손안의 AI비서, 추천 알고리듬](https://brunch.co.kr/@kakao-it/72#comment)
- [2017, [카카오AI리포트]카카오I 추천 엔진의 진화](https://brunch.co.kr/@kakao-it/136)
- [2017, [카카오AI리포트]더욱 똑똑해진 AI 광고 알고리듬](https://brunch.co.kr/@kakao-it/84)
- [2016, 루빅스(RUBICS) – kakao의 실시간 추천 시스템](https://tech.kakao.com/2016/04/27/rubics/)

## Linked in
- [2020, Understanding dwell time to improve LinkedIn feed ranking](https://engineering.linkedin.com/blog/2020/understanding-feed-dwell-time)

## LINER
- [2023, 나만의 정보탐색 파트너, 브라우징 코파일럿 RAG 도입기](https://blog.getliner.com/%eb%82%98%eb%a7%8c%ec%9d%98-%ec%a0%95%eb%b3%b4%ed%83%90%ec%83%89-%ed%8c%8c%ed%8a%b8%eb%84%88-%eb%b8%8c%eb%9d%bc%ec%9a%b0%ec%a7%95-%ec%bd%94%ed%8c%8c%ec%9d%bc%eb%9f%bf-rag-%eb%8f%84%ec%9e%85%ea%b8%b0/)
- [2023, 너, 내 비서가 돼라! LINER Autonomous Agent 구축기](https://blog.getliner.com/liner-autonomous-agent/)
- [2023, WorkSpace, Autonomous Agent in LINER](https://blog.getliner.com/workspace-autonomous-agent-in-liner/)
- [2023, 나를 가장 잘 이해하는 개인화 어시스턴트, LINER Copilot](https://blog.getliner.com/liner-copilot/)
- [2023, Graph in LINER : 글로벌 추천 프로덕트에 GNN 적용하기](https://blog.getliner.com/graph-in-liner/)
- [2023, Human-AI Co-Learning을 향해! 강화학습 기반 랭킹 모델 도입기](https://blog.getliner.com/mab-based-ranker/)
- [2023. TikTok for Text! 라이너 앱 Session-based Recommender 구축기](https://blog.getliner.com/sessrec/)
- [2023, LINER Chat: From Prompt Engineering to Plugins](https://blog.getliner.com/liner-chat/)
- [2023, Sequential Recommenders: 기계들과 회로들](https://blog.getliner.com/sequential-recommenders/)
- [2022, 그 많던 벡터는 다 어디로 갔을까? Milvus 활용기](https://blog.getliner.com/milvus-usage/)
- [2022, 콜드 스타트 사용자에게 콘텐츠 추천을 ! – 트렌드 기반 추천](https://blog.getliner.com/trending-recommendation/)
- [2022, LINERVA-WALK : Pixie 기반 랜덤 워크 추천 모델링](https://blog.getliner.com/linerva-walk/)
- [2022, 콘텐츠 기반 필터링에 PinnerSage 입히기](https://blog.getliner.com/pinnersage/)
- [2022, Bag-of-Tricks for Recommendation: Recency, Clustering 그리고 Item Shuffling](https://blog.getliner.com/bag-of-tricks-for-recommendation-recency-clustering-%ea%b7%b8%eb%a6%ac%ea%b3%a0-item-shuffling/)
- [2022, 컨텐츠 기반 필터링 구축기: MiniLM, ScaNN 그리고 TFServing](https://blog.getliner.com/%ec%bb%a8%ed%85%90%ec%b8%a0-%ea%b8%b0%eb%b0%98-%ed%95%84%ed%84%b0%eb%a7%81-%ea%b5%ac%ec%b6%95%ea%b8%b0-minilm-scann-%ea%b7%b8%eb%a6%ac%ea%b3%a0-tfserving/)
- [2021, 양질 중 우선시 되어야 하는 것은? Quality!](https://blog.getliner.com/%ec%96%91%ec%a7%88-%ec%a4%91-%ec%9a%b0%ec%84%a0%ec%8b%9c-%eb%90%98%ec%96%b4%ec%95%bc-%ed%95%98%eb%8a%94-%ea%b2%83%ec%9d%80-quality/)
- [2020, 하이라이팅 구문을 통한 추천 API 구현](https://blog.getliner.com/recommendation-api-using-highlights/)
- [2020, 유해 키워드 필터링 문제와 해결](https://blog.getliner.com/word-filtering-experience/)

## NAVER
- [2023, 언어 모델 기반의 범용 유저 임베딩과 이를 활용한 추천시스템 및 광고 타겟팅](https://deview.kr/2023/sessions/539)
- [2023, 상황에 맞는 취향 장소 발견하기. HyperLocal 추천 시스템 A to Z](https://deview.kr/2023/sessions/553)
- [2023, 지금까지 이런 검색은 없었다. 이것은 검색인가 추천인가 - 네이버 스마트블록 개인화 검색](https://deview.kr/2023/sessions/563)
- [2023, 네이버 검색은 어떻게 나보다 더 내 의도를 잘 아는가? : AiRSearch 반응형 추천](https://deview.kr/2023/sessions/546)
- [2021, Bring Your Own Data: Business AI 고민? HyperCLOVA에게 무엇이든 물어보살](https://deview.kr/2021/sessions/434)
- [2021, 네이버 뉴스 추천 알고리즘에 대해 (Part1)](https://blog.naver.com/PostView.naver?blogId=naver_search&logNo=222439351406&parentCategoryNo=&categoryNo=52&viewDate=&isShowPopularPosts=false&from=postView)
- [2021, 네이버 뉴스 추천 알고리즘에 대해 (Part2)](https://blog.naver.com/PostView.naver?blogId=naver_search&logNo=222439504418&parentCategoryNo=&categoryNo=52&viewDate=&isShowPopularPosts=false&from=postView)
- [2021, 네이버 뉴스 추천 알고리즘에 대해 (Part3)](https://blog.naver.com/PostView.naver?blogId=naver_search&logNo=222439512532&parentCategoryNo=&categoryNo=52&viewDate=&isShowPopularPosts=false&from=postView)
- [2020, LINE Timeline의 새로운 도전 1편 – 추천 컨텐츠 탐색을 위한 Discover와 새로운 구독 모델 Follow](https://engineering.linecorp.com/ko/blog/a-new-challenge-for-line-timeline-1/?fbclid=IwAR2rLnvgLGajXFyR2Pin9Hoez0GfxJUUSSkGqZK3PJdW7R-fLcBJZbWAfKk)
- [2020, LINE Timeline의 새로운 도전 2편 – Discover 딜리버리 시스템 소개](https://engineering.linecorp.com/ko/blog/line-timeline-discover-delivery-system/)
- [2020, LINE Timeline의 새로운 도전 3편 – Discover 추천 모델](https://engineering.linecorp.com/ko/blog/line-timeline-discover-ml-recommendation/)
- [2017, CF기술•RNN기술의 장점이 융합된 ‘AiRS(에어스)’가 모바일 뉴스판에 시범 적용됩니다](https://blog.naver.com/naver_search/221105431207)
- [2017, 인공지능 추천 시스템 AiRS 개발기: 모델링과 시스템](https://tv.naver.com/v/2297146)
- [2017, 똑똑한 인공지능 DJ 비서 Clova Music](https://tv.naver.com/v/2302007)

## Pinterest
- [2021, SearchSage: Learning Search Query Representations at Pinterest](https://medium.com/pinterest-engineering/searchsage-learning-search-query-representations-at-pinterest-654f2bb887fc)
- [2020, PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest](https://medium.com/pinterest-engineering/pinnersage-multi-modal-user-embedding-framework-for-recommendations-at-pinterest-bfd116b49475)
- [2018, [스타트업in] 핀터레스트, "어머 이거 수집해야돼" 수집 욕구 자극하는 직관적인 SNS ](https://www.dailypop.kr/news/articleView.html?idxno=35268)
- [2018, PinSage: A new graph convolutional neural network for web-scale recommender systems](https://medium.com/pinterest-engineering/pinsage-a-new-graph-convolutional-neural-network-for-web-scale-recommender-systems-88795a107f48)
- [2018, An update on Pixie, Pinterest’s recommendation system](https://medium.com/pinterest-engineering/an-update-on-pixie-pinterests-recommendation-system-6f273f737e1b)
- [2017, Introducing Pixie, an advanced graph-based recommendation system](https://medium.com/pinterest-engineering/introducing-pixie-an-advanced-graph-based-recommendation-system-e7b4229b664b)
- [2017, Applying deep learning to Related Pins](https://medium.com/the-graph/applying-deep-learning-to-related-pins-a6fee3c92f5e#.m1zyskflp)
- [2016, Pinterest로 알아보는 스타트업 전략](https://brunch.co.kr/@davidjkpark/10)
- [2015, Pinnability: Machine learning in the home feed](https://medium.com/pinterest-engineering/pinnability-machine-learning-in-the-home-feed-64be2074bf60)
- [blog](https://medium.com/@Pinterest_Engineering)

## Riiid
- [2020, AI 프로젝트 실패의 5가지 원인](https://medium.com/riiid-teamblog-kr/ai-%ED%94%84%EB%A1%9C%EC%A0%9D%ED%8A%B8%EB%A5%BC-%EC%8B%A4%ED%8C%A8%ED%95%98%EA%B2%8C-%EB%A7%8C%EB%93%9C%EB%8A%94-5%EA%B0%80%EC%A7%80-%EC%9D%B4%EC%9C%A0-8e7813cf0a95)

## Spotify
- [2022, Spotify의 음악 추천이 매력적인 이유](https://playinpap.github.io/recsys-spotify/)
- [2022, Modeling Users According to Their Slow and Fast-Moving Interests](https://research.atspotify.com/modeling-users-according-to-their-slow-and-fast-moving-interests/?fbclid=IwAR1bvYJv1noUBZej_s1JRHQFAUwRx4udXgwy8_FFbOMqU86DF37oBhuLu94)
- [2022, Mostra: Balancing multiple objectives for music recommendation](https://research.atspotify.com/mostra-balancing-multiple-objectives-for-music-recommendation/?fbclid=IwAR2ZWhn6FIOzAFjgysi0yLMjaMqp0f74S7Kk5UuGXVFf9msdHReADF8jGis)
- [2021, 스포티파이: 맞춤형 플레이리스트 개발 비하인드 스토리](https://yozm.wishket.com/magazine/detail/1322/)

## Twitter
- [2023, Twitter's Recommendation Algorithm](https://blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm)

## TikTok
- [2022, 틱톡 추천(For You Page) 알고리즘의 비밀](https://excitingfx.kr/tiktoksecretalgorithm/)
- [2020, How TikTok recommends videos #ForYou](https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you/)

## Upstage
- [2022, 추천 AI Pack 개발로 고객 성공 사례를 만들다 - [스타뷰 Vol. 3] RecSys & AI Challenges팀](https://www.upstage.ai/blog/people/recsys-ai-challenges-interview)
- [2022, 커머스 플랫폼 빌더 ‘(주)브랜디’가 말하는 플랫폼 성장의 원동력, ‘AI 추천 기술’ - (주)브랜디 데이터최적화실장 최원조님 인터뷰](https://www.upstage.ai/blog/business/customer-success-stories-brandi)
- [2022, [AI 트렌드] 매출 성장의 비결로 떠오른 검색· 추천 기술, Seargest](https://www.upstage.ai/blog/insight/2023-ai-tech-trend-seargest)

## WATCHA
- [2021, 1년차 주니어가 추천 시스템 현장에서 마주한 고민](https://medium.com/watcha/1%EB%85%84%EC%B0%A8-%EC%A3%BC%EB%8B%88%EC%96%B4%EA%B0%80-%EC%B6%94%EC%B2%9C-%EC%8B%9C%EC%8A%A4%ED%85%9C-%ED%98%84%EC%9E%A5%EC%97%90%EC%84%9C-%EB%A7%88%EC%A3%BC%ED%95%9C-%EA%B3%A0%EB%AF%BC-12f568eb2cc2)
- [2021, 추천 서비스가 컨테이너를 만났을 때-Part 1](https://medium.com/watcha/part-1-%EC%B6%94%EC%B2%9C-%EC%84%9C%EB%B9%84%EC%8A%A4%EA%B0%80-%EC%BB%A8%ED%85%8C%EC%9D%B4%EB%84%88%EB%A5%BC-%EB%A7%8C%EB%82%AC%EC%9D%84-%EB%95%8C-f886d0f08df)
- [2021, 추천 서비스가 컨테이너를 만났을 때-Part 2](https://medium.com/watcha/%EC%B6%94%EC%B2%9C-%EC%84%9C%EB%B9%84%EC%8A%A4%EA%B0%80-%EC%BB%A8%ED%85%8C%EC%9D%B4%EB%84%88%EB%A5%BC-%EB%A7%8C%EB%82%AC%EC%9D%84-%EB%95%8C-part-2-775a1e00795d)

## 당근마켓
- [2024, 당근 홈피드의 진화와 역사 | 당근 ML 밋업 1회](https://www.youtube.com/watch?v=TcG3JjPIXVc&list=PLaHcMRg2hoBqSQM48ospyb9hQTzzvMF4y&index=2)
- [2024, LLM을 프로덕션에 적용하며 배운 것들 | 당근 ML 밋업 1회](https://www.youtube.com/watch?v=NzxlIGPbICY&list=PLaHcMRg2hoBqSQM48ospyb9hQTzzvMF4y&index=3)
- [2024, 당근 ML인프라 A to Z : 현재와 미래 | 당근 ML 밋업 1회](https://www.youtube.com/watch?v=gTXBr2r1104&list=PLaHcMRg2hoBqSQM48ospyb9hQTzzvMF4y&index=4)
- [2019, 딥러닝 개인화 추천](https://medium.com/daangn/%EB%94%A5%EB%9F%AC%EB%8B%9D-%EA%B0%9C%EC%9D%B8%ED%99%94-%EC%B6%94%EC%B2%9C-1eda682c2e8c)
- [2019, 딥러닝 추천 시스템 in production](https://medium.com/daangn/%EB%94%A5%EB%9F%AC%EB%8B%9D-%EC%B6%94%EC%B2%9C-%EC%8B%9C%EC%8A%A4%ED%85%9C-in-production-fa623877e56a)

## 마이리얼트립
- [2020, 추천 서비스와 아키텍처[1]-추천 서비스란 무엇인가](https://medium.com/myrealtrip-product/%EC%B6%94%EC%B2%9C-%EC%84%9C%EB%B9%84%EC%8A%A4%EC%99%80-%EC%95%84%ED%82%A4%ED%85%8D%EC%B3%90-1-40ddb5e7e3bd)
- [2020, 추천 서비스와 아키텍처[2]-추천 서비스 제공을 위한 아키텍처](https://medium.com/myrealtrip-product/%EC%B6%94%EC%B2%9C-%EC%84%9C%EB%B9%84%EC%8A%A4%EC%99%80-%EC%95%84%ED%82%A4%ED%85%8D%EC%B2%98-2-%EC%B6%94%EC%B2%9C%EC%84%9C%EB%B9%84%EC%8A%A4-%EC%A0%9C%EA%B3%B5%EC%9D%84-%EC%9C%84%ED%95%9C-%EC%95%84%ED%82%A4%ED%85%8D%EC%B2%98-9897d6dc8bc9)

## 번개장터
- [2019, 번개장터 추천 시스템 개발 및 도입후기 Part1](https://www.theteams.kr/teams/7937/post/70672)
- [2019, 번개장터 추천 시스템 개발 및 도입후기 Part2](https://www.theteams.kr/teams/7937/post/70673)
- [2019, 번개장터 추천 시스템 개발 및 도입후기 Part3](https://www.theteams.kr/teams/7937/post/70674)
- [2019, 번개장터 CPC 광고 도입 후기 Part1](https://www.theteams.kr/teams/7937/post/70677)
- [2019, 번개장터 CPC 광고 도입 후기 Part2](https://www.theteams.kr/teams/7937/post/70684)

## 스타일쉐어
- [2021, 유저들이 정말로 원하는 컨텐츠는 무엇일까? — 우리의 추천시스템의 시작 (1)](https://medium.com/styleshare/styleshare-recommendation-beginning-1-c70e159e7479)
- [2021, 유저들이 정말로 원하는 컨텐츠는 무엇일까? — 우리의 추천시스템의 시작 (2)](https://medium.com/styleshare/styleshare-recommendation-beginning-2-74aa50c84510)

## 오늘의 집
- [2023, 유사 이미지 추천 개발 #2 비슷한 상품](https://www.bucketplace.com/post/2023-07-13-%EC%9C%A0%EC%82%AC-%EC%9D%B4%EB%AF%B8%EC%A7%80-%EC%B6%94%EC%B2%9C-%EA%B0%9C%EB%B0%9C-2-%EB%B9%84%EC%8A%B7%ED%95%9C-%EC%83%81%ED%92%88/)
- [2023, 유사 이미지 추천 개발 #1 비슷한 공간](https://www.bucketplace.com/post/2023-05-22-%EC%9C%A0%EC%82%AC-%EC%9D%B4%EB%AF%B8%EC%A7%80-%EC%B6%94%EC%B2%9C-%EA%B0%9C%EB%B0%9C-1-%EB%B9%84%EC%8A%B7%ED%95%9C-%EA%B3%B5%EA%B0%84/)

## 요기요
- [2022, [알잘딱깔센 추천 모델 만들기] — GNN을 활용한 요기요의 추천 모델 YoSEMITE](https://techblog.yogiyo.co.kr/%EC%95%8C%EC%9E%98%EB%94%B1%EA%B9%94%EC%84%BC-%EC%B6%94%EC%B2%9C-%EB%AA%A8%EB%8D%B8-%EB%A7%8C%EB%93%A4%EA%B8%B0-gnn%EC%9D%84-%ED%99%9C%EC%9A%A9%ED%95%9C-%EC%9A%94%EA%B8%B0%EC%9A%94%EC%9D%98-%EC%B6%94%EC%B2%9C-%EB%AA%A8%EB%8D%B8-yosemite-33b0600d2464)
- [2021, [돈까스 좋아해요?] — 요기요 ML엔지니어의 3개월](https://techblog.yogiyo.co.kr/%EB%8F%88%EA%B9%8C%EC%8A%A4-%EC%A2%8B%EC%95%84%ED%95%B4%EC%9A%94-%EC%9A%94%EA%B8%B0%EC%9A%94-ml%EC%97%94%EC%A7%80%EB%8B%88%EC%96%B4%EC%9D%98-3%EA%B0%9C%EC%9B%94-4ff2af439487)

## 그 외 자료
### 블로그 및 모읍집
- [Y.LAB](https://yamalab.tistory.com/category/Recommender%20System/%EC%B6%94%EC%B2%9C%20%EC%8B%9C%EC%8A%A4%ED%85%9C?page=1)
- [GBRS LAB](https://modulabs-hub.oopy.io/6d2d2394-92fb-4715-a132-3d9f3e5b25a8)
- [BRS LAB](https://modulabs-hub.oopy.io/ab1646ce-7b60-4ef6-8d69-1eb009684f07)

### 2022
- [2022, The recommender revolution](https://www.technologyreview.com/2022/04/27/1048517/the-recommender-revolution/)
- [2022, [Translation] Recommender Systems, Not Just Recommender Models](https://higee.io/translation-recommender-systems-not-just-recommender-models-c024a97d7d74)
- [2022, 프롬 특강 - 신뢰할 수 있는 A/B 테스트를 위해 알아야할 것들 (이민호)](https://www.youtube.com/watch?v=5nXu1E0m4e0)
- [2022, 베이지안 A/B 테스트 - Bayes theorem](https://playinpap.github.io/bayesian_ab_test_bayes_theroem/)
- [2022, 넷플릭스의 A/B Testing - 거짓 음성과 검정력](https://playinpap.github.io/netflix-ab-testing-fn/)
- [2022, 넷플릭스에서 A/B 테스트를 해석하는 법: 거짓 양성 오류 알아보기](https://playinpap.github.io/interpreting-a/b-test-results-false-positives/)
- [2022, 선추천후감상- Learning Recommendation](https://playinpap.github.io/reco-first-enjoy-after-learning-recommendation/)

### 2021
- [2021, System Design for Recommendations and Search](https://eugeneyan.com/writing/system-design-for-discovery/)
- [2021, Modern Recommender Systems](https://towardsdatascience.com/modern-recommender-systems-a0c727609aa8)
- [2021, Item2Item: Wider and Deeper Product Recommendation](https://tv.naver.com/v/20311903/list/709884)
- [2021, Overview Negative Sampling on Recommendation Systems](https://medium.com/mlearning-ai/overview-negative-sampling-on-recommendation-systems-230a051c6cd7)
- [2021, ‘별점’의 함정, 무엇이 문제인가](https://zdnet.co.kr/view/?no=20210524104310)
- [2021, ‘별점’의 해결책 "집단지성 메커니즘 제대로 작동해야"](https://zdnet.co.kr/view/?no=20210531153513)
- [2021, AI 알고리즘의 딜레마...‘선택의 역설’vs‘에코챔버’·‘필터버블’](https://zdnet.co.kr/view/?no=20210621135006)
- [2021, How Can Recommender Systems Contribute to Mitigate Echo Chambers and Filter Bubbles?](https://takuti.me/note/recsys-2021-echo-chambers-and-filter-bubbles/)

### 2020
- [2020, 뭐볼까? : 네이버 AiRS 인공지능 콘텐츠 추천의 진화](https://tv.naver.com/v/16968202?query=NAVER+Engineering%2C+%EC%B6%94%EC%B2%9C%EC%8B%9C%EC%8A%A4%ED%85%9C&plClips=false:16137697:16968206:19499805:15624686:15436151:16970750:19451057:18582667:19149564:15841749:18387813:4580088:14347749:14951460:19188921:1543714:3144570:16968208:16972082:16971866:16972084:5874822:16366105:16405099:19244453:19040189:2297146:19489169:18996671:15776820:18634923:16968202:19248843:19223749:16028986:18973670:19028868:18514575:14711496:17022125)
- [2020, 추천시스템 3.0: 딥러닝 후기시대에서 바이어스, 그래프, 그리고 인과관계의 중요성](https://tv.naver.com/v/16970750?query=NAVER+Engineering%2C+%EC%B6%94%EC%B2%9C%EC%8B%9C%EC%8A%A4%ED%85%9C&plClips=false:16137697:16968206:19499805:15624686:15436151:16970750:19451057:18582667:19149564:15841749:18387813:4580088:14347749:14951460:19188921:1543714:3144570:16968208:16972082:16971866:16972084:5874822:16366105:16405099:19244453:19040189:2297146:19489169:18996671:15776820:18634923:16968202:19248843:19223749:16028986:18973670:19028868:18514575:14711496:17022125)
- [2020, 당신 취향의 맛집을 추천해드립니다 : 장소 개인화 추천 시스템의 비밀](https://tv.naver.com/v/16968206?query=NAVER+Engineering%2C+%EC%B6%94%EC%B2%9C%EC%8B%9C%EC%8A%A4%ED%85%9C&plClips=false:16137697:16968206:19499805:15624686:15436151:16970750:19451057:18582667:19149564:15841749:18387813:4580088:14347749:14951460:19188921:1543714:3144570:16968208:16972082:16971866:16972084:5874822:16366105:16405099:19244453:19040189:2297146:19489169:18996671:15776820:18634923:16968202:19248843:19223749:16028986:18973670:19028868:18514575:14711496:17022125)
- [2020, 유저가 좋은 작품(웹툰)를 만났을 때](https://tv.naver.com/v/16968269) <- 강추
- [2020, 딥러닝 모델 Serving 간단 구축기](https://tech.socarcorp.kr/data/2020/03/10/ml-model-serving.html)

### 2019
- [2019, 추천시스템, 이제는 돈이 되어야 한다.](https://www.youtube.com/watch?v=RK3-aNWveMs) <- 강추
- [2019, Recent Advances in Deep Recommender Systems](https://tv.naver.com/v/9976771?query=NAVER+Engineering%2C+%EC%B6%94%EC%B2%9C%EC%8B%9C%EC%8A%A4%ED%85%9C&plClips=false:8222729:8222640:17080622:18403041:18870581:18790614:18952902:17522658:18683564:8222555:16332316:11602269:18754236:8222518:16365186:16365062:10363799:16255370:14388400:18282841:9976771:17409268:8222700:14590656:17378255:16511099:18792603:19224013:18639482:18088082:8222610:18960196:19495120:8222553:8222728:18438817:8222748:13515470:19396298:8222746)
- [2019, 개인화 서비스는 어떻게 만드나요? - 1편](https://brunch.co.kr/@plusx/30)
- [2019, 개인화 서비스는 어떻게 만드나요? - 2편](https://brunch.co.kr/@plusx/32)
- [2019, 쿠팡 추천 시스템 2년간의 변천사 (상품추천에서 실시간 개인화로)](https://tv.naver.com/v/11212875)

### 2018
- [2018, 눈으로 듣는 음악 추천 시스템](https://tv.kakao.com/channel/3150758/cliplink/391418802) <- 강추
- [2018, 눈으로 듣는 음악 추천 시스템 + a](https://brunch.co.kr/@goodvc78/20) <- 강추
- [2018, 추천 시스템을 위한 어플리케이션 서버 개발 후기](https://www.youtube.com/watch?v=6oOQJtLa14U)
- [2018, Word2vec을 응용한 컨텐츠 클러스터링](https://brunch.co.kr/@mobiinside/950)
- [2018, lightweight approximate Nearest Neighbor library](https://tv.kakao.com/channel/3150758/cliplink/391419278)

### 2017
- [2017, Word2Vec 그리고 추천 시스템의 Item2Vec](https://brunch.co.kr/@goodvc78/16) <- 강추
- [2017, Data science with R - 1. 오해](https://blog.ab180.co/posts/data-science-with-r-1-misperception)
- [2017, Data science with R - 2. 데이터 시각화](https://blog.ab180.co/posts/data-science-with-r-2-data-visualization)
- [2017, Data science with R - 3. 최적화](https://blog.ab180.co/posts/data-science-with-r-3-optimization)
- [2017, Data science with R - 4. 멀티 터치 기여도 모델](https://blog.ab180.co/posts/data-science-with-r-4-multi-touch-attribution)
- [2017, Data science with R - 5. 크로스 디바이스 매칭(Cross Device Matching)](https://blog.ab180.co/posts/data-science-with-r-5-cross-device-matching)
- [2017, Data science with R - 6. RTB란?](https://blog.ab180.co/posts/data-science-with-r-6-rtb)

### 2015
- [2015, 추천시스템이 word2vec을 만났을때](https://www.youtube.com/watch?v=iutEgQg7yws) <- 강추

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