Seonglae ChoRecSys
The main goal is to alleviate item data sparsity and Cold Start problems. We address sparsity through Graph Recommender system, zero injection, or preference modeling. For cold start issues, we use Content-Based Filtering approaches.
Rather than focusing on SOTA, it's important to try various models optimized for the service using feedback from A/B testing. Overall, Collaborative Filtering like embedding-based approaches are outdated, with modern systems mainly focusing on Multi-stage recommendation with Candidate Generation and Ranking Model.
Input
Output is top-k recommendation
- Content Space - Content Latent Vector
- User Representation (session or user interaction history) - User Latent Vector
Scaling ability for the above two axes while maintaining minimal response time is important.
Search & Recommend System are in a same spectrum
Recommender Systems
Recommender System Notion
Recommender System Engines
Great overview
Introducing TensorFlow Recommenders
Introducing TensorFlow Recommenders, a library for building flexible and powerful recommender models.
https://blog.tensorflow.org/2020/09/introducing-tensorflow-recommenders.html
Tutorial
RecSys2021 Tutorial
Abstract Counterfactual estimators enable the use of existing log data to estimate how some new target recommendation policy would have performed, if it had been used instead of the policy that logged the data. We say that those estimators work "off-policy", since the policy that logged the data
https://sites.google.com/cornell.edu/recsys2021tutorial
Pixel Screenshots is a feature that uses screenshots users take themselves instead of recording.
Exclusive: This is Google AI, and it's coming to the Pixel 9
Google Pixel 9 will gain new AI capabilities, including a feature resembling the controversial Windows Recall. Read the details here.
https://www.androidauthority.com/google-ai-recall-pixel-9-3456399/
www.theinsaneapp.com
https://www.theinsaneapp.com/2021/03/system-design-and-recommendation-algorithms.html
커리어 성장을 위한 HR 플랫폼 | 러닝스푼즈
커리어 성장을 위한 HR 플랫폼. 데이터 사이언스, 파이낸스, 부동산 금융, 퍼포먼스 마케팅 등 다양한 분야에서 최고의 실력을 지닌 실무진들이 온/오프라인 방식으로 콘텐츠를 제공하고 있습니다.
https://learningspoons.com/course/detail/recommandsys/
