implicit

Fast Python Collaborative Filtering for Implicit Feedback Datasets

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benfred

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3,778

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626

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Created

Apr 17, 2016

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README Summary

Implicit is a fast Python library for collaborative filtering with implicit feedback datasets, implementing algorithms like Alternating Least Squares (ALS), Bayesian Personalized Ranking (BPR), and Logistic Matrix Factorization. The library is optimized for speed using Cython and provides GPU acceleration support for handling large-scale recommendation systems. It's designed specifically for implicit feedback data where user preferences are inferred from behavior rather than explicit ratings.

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Collaborative FilteringMatrix FactorizationAlternating Least Squares (ALS)Implicit Feedback ModelingRecommendation SystemsSparse Matrix OperationsGPU Computing with CUDABayesian Personalized Ranking

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Collaborative FilteringMatrix FactorizationAlternating Least Squares (ALS)Implicit Feedback ModelingRecommendation SystemsSparse Matrix OperationsGPU Computing with CUDABayesian Personalized RankingGamingTabularSelf-hostedContent RecommendationsSimilar Item DiscoveryMovie RecommendationPublishingMusic StreamingCloud APIPersonalized Content DeliveryProduct RecommendationsSocial MediaE-commerceUser Behavior AnalysisMusic RecommendationVideo StreamingEntertainment & MediaOn-premisePersonalizationReal-time RecommendationsPython

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Updated 1 years ago

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production

Quality: medium
Maturity: production

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Python100.0%

Timeline

Project created: Apr 17, 2016
Forked: Mar 22, 2026
Your last push: 1 years ago
Upstream last push: 1 years ago
Tracked since: Jul 11, 2024

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