Recommender System
A Recommender System (a.k.a. Collaborative Filtering, Social Filtering, and Social Information Filtering) is an information filtering technique that takes details and data associated with a user's profile and compares it with similar data (habits, likes, opinions, etc.) of other users on the same service in order to present recommendations of what might be of interest to the original user.
Examples of web services that make use of Recommender Systems are: online auction/ecommerce websites, music services, movie and television show streaming services, etc.
- Frequently bought together for Retail
- Top picks for you for Media and Entertainment
https://www.quora.com/LinkedIn-Recommendations/How-does-LinkedIns-recommendation-system-work
https://docs.aws.amazon.com/personalize/latest/dg/what-is-personalize.html
Vinija's Notes • Recommendation Systems • Research Papers
Social Media Recommendation Engine
- Doom scrolling
- Endless bottom / endless scrolling
- Reel life vs real life
Price Recommendation Engine
Links
- A Spotify Song and Playlist Recommendation Engine | MongoDB
- GitHub - recommenders-team/recommenders: Best Practices on Recommendation Systems
- GitHub - jihoo-kim/awesome-RecSys: A curated list of awesome Recommender System (Books, Conferences, Researchers, Papers, Github Repositories, Useful Sites, Youtube Videos)
- GitHub - facebookresearch/dlrm: An implementation of a deep learning recommendation model (DLRM)
- GitHub - USTC-StarTeam/Awesome-Large-Recommendation-Models: 🔥🔥🔥 Latest Advances on Large Recommendation Models
- GitHub - archersama/awesome-recommend-system-pretraining-papers: Paper List for Recommend-system PreTrained Models