Search Results for "collaborative filtering"

This project provides fast Python implementations of several different popular recommendation algorithms for implicit feedback datasets. All models have multi-threaded training routines, using Cython and OpenMP to fit the models in parallel among all available CPU cores. In addition, the ALS and BPR models both have custom CUDA kernels - enabling fitting on compatible GPU’s. This library also supports using approximate nearest neighbour libraries such as Annoy, NMSLIB and Faiss for speeding...

Cherche allows the creation of efficient neural search pipelines using retrievers and pre-trained language models as rankers. Cherche's main strength is its ability to build diverse and end-to-end pipelines from lexical matching, semantic matching, and collaborative filtering-based models. Cherche provides modules dedicated to summarization and question answering. These modules are compatible with Hugging Face's pre-trained models and fully integrated into neural search pipelines. Search is fully compatible with the collaborative filtering library Implicit. It is advantageous if you have a history associated with users and you want to retrieve / re-rank documents based on user preferences.

MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. * More info + downloads: https://mlpack.org * Git repo: https://github.com/mlpack/mlpack

Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics. This repo is dedicated to engineers, developers, data scientists and all other professions that take interest in AI, ML, DL and related sciences. To make learning interesting and to create a place to easily find all the necessary material. Please contribute, watch, star, fork and share the repo with others in your community.

The experimental source code for the paper, "A Novel Recommendation Approach Based on Chronological Cohesive Units in Content Consuming"