Search Results for "recommendation"

The Algorithm is Twitter’s open source release of the core ranking system that powers the platform’s home timeline. It provides transparency into how tweets are selected, prioritized, and surfaced to users, reflecting Twitter’s move toward openness in recommendation algorithms. The repository contains the recommendation pipeline, which incorporates signals such as engagement, relevance, and content features, and demonstrates how they combine to form ranked outputs. Written primarily in Scala, it shows the architecture of large-scale recommendation systems, including candidate sourcing, ranking, and heuristics. ...

X For You Feed Algorithm is the open-sourced core recommendation system that powers the For You feed on X (the social network formerly known as Twitter), and it represents one of the first times a major social platform has published production-level ranking code for public review and experimentation. The repository contains the full pipeline that ingests user engagement and content candidate data, processes it through retrieval, hydration, filtering, scoring, and selection layers, and ultimately ranks posts to show what appears in a user’s feed. ...

...There is also a tutorial provided on undirected graphs. Some tutorials are problem oriented and show how to compute the winner of an election, how to build a best choice recommendation, or how to linearly rank or rate with multiple incommensurable performance criteria. Other tutorials concern more specifically operational aspects of computing maximal independent sets (MISs) and kernels in graphs and digraphs.

...Most of the programming navigation websites are in disrepair and have good navigation, but they are limited in search and classification, and they do not have functions such as self-recommendation and liking, so they are not sustainable.

TER implements the CCSDS Recommendation for Image Data Compression (CCSDS 122.0-B-1). TER also adds new features to the Recommendation. TER is designed and programmed with the aim to provide a good basis to test and develop the CCSDS Recommendation.

Multi-categorization Recommendation Adjusting (MRA) is to optimize the results of recommendation based on traditional(basic) recommendation models, through introducing objective category information and taking use of the feature that users always get the habits of preferring certain categories. Besides this, there are two advantages of this improved model: 1) it can be easily applied to any kind of existing recommendation models.

The Idealize Recommendation Framework supports the development of recommender systems. We have devised a set of requirements all recommender systems must address, an architecture for those requirements, a technical vocabulary and a production model.

TiVo style recommendation engine for MythTV. MythMagic selects and automatically records shows from your program guide based on previous viewing habits. Recordings can be accepted or rejected to improve recommendation accuracy.

Design and develop Recommendation and Adaptive Prediction Engines to address eCommerce opportunities. Build a portfolio of engines by creating and porting algorithms from multiple disciplines to a usable form. Try to solve NetFlix and other challenges.