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. ...

...This makes it easy to create high-performing search applications at any scale, whether you want to use traditional techniques or a modern vector-based approach. You can even combine both approaches efficiently in the same query, something no other engine can do. Recommendation, personalization and targeting involves evaluating recommender models over content items to select the best ones. Vespa lets you build applications which does this online, typically combining fast vector search and filtering with evaluation of machine-learned models over the items. This makes it possible to make recommendations specifically for each user or situation, using completely up to date information.

...It includes approaches such as edit distance, cosine similarity, Euclidean distance, Jaccard similarity, Jaro distance, Jaro-Winkler distance, Manhattan distance, SimHash with Hamming distance, and Sørensen-Dice coefficient. It also supports Java dependency integration through Maven or Gradle workflows. It is useful for Chinese NLP projects, search features, duplicate detection, recommendation systems, and text analysis experiments.

...The current version is 1.0.x. It is very close to the recommendation with all related tests of the XProc Test Suite passed.

This package implements the FF1, FF3, and FFX algorithms and the A2 and A10 parameter sets for format-preserving encryption. The FF1 and FF3 methods for format-preserving encryption are implementations of NIST Special Publication 800-38G, Recommendation for Block Cipher Modes of Operation: Methods for Format-Preserving Encryption. The FFX algorithm and A2 and A10 parameter sets are implementations of "The FFX Mode of Operation for Format-Preserving Encryption," by Mihir Bellare, Phillip Rogaway, and Terence Spies. For more information see the README.txt and package-info.java files in the source.

LibRec is a Java library for recommender systems (Java version 1.7 or higher required). It implements a suit of state-of-the-art recommendation algorithms, aiming to resolve two classic recommendation tasks: rating prediction and item ranking.

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.

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.

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.

JBind is a data binding framework linking Java and XML. The framework consists of a schema compiler for generating Java sources and a runtime environment. JBind supports most of the W3C XML schema recommendation of 2 May 2001.

Processor Interface for XML Inclusions (PIXI) is an implementation of the W3C XInclude Recommendation in Java.