Search Results for "recommender"

A framework for learning from a continuous supply of examples, a data stream. Includes classification, regression, clustering, outlier detection and recommender systems. Related to the WEKA project, also written in Java, while scaling to adaptive large scale machine learning.

Transformers4Rec is an advanced recommendation system library that leverages Transformer models for sequential and session-based recommendations. The library works as a bridge between natural language processing (NLP) and recommender systems (RecSys) by integrating with one of the most popular NLP frameworks, Hugging Face Transformers (HF). Transformers4Rec makes state-of-the-art transformer architectures available for RecSys researchers and industry practitioners. Traditional recommendation algorithms usually ignore the temporal dynamics and the sequence of interactions when trying to model user behavior. ...

Albedo is an open-source recommender system aimed at helping developers discover GitHub repositories by learning from activity signals. It treats repositories and developers as a graph of interactions and applies large-scale matrix factorization to model affinities, with Apache Spark providing the distributed data processing. The project focuses on implicit feedback—stars, watches, and other engagement metrics—so it can build useful recommendations without explicit ratings.

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.

...It builds upon the Grails Web Framework and is developed at GESIS. It implements two main functionalities: (1) A number of showcases that show the implemented services like a so-called Search Term Recommender and different science-model based ranking mechanisms and (2) an IR assessment module that let's one do an interactive evaluation of the retrieval services. All implemented services are available via well-documented RESTful API. This toolkit is distributed under an Apache License 2.0.

The Duine Framework allows one to develop prediction engines for recommender systems. It contains a set of prediction techniques, a way to combine these techniques and a profile manager. The framework has a plug-in architecture, allowing customization.