Lightfm

Lightfm - build hybrid recommendation engines | Awesome Repos

Features

Recommender Systems - Provides a hybrid recommendation system combining collaborative filtering with content-based metadata.
Interaction Matrix Factorizers - Uses latent-factor matrix factorization to decompose user-item interactions into shared embedding spaces.
Hybrid Matrix Factorization - Learns latent representations of users and items using hybrid matrix factorization.
Recommendation Engines - Implements a hybrid recommendation engine that integrates interaction data with user and item metadata.
Implicit Feedback Models - Optimizes item preferences using WARP and BPR loss functions for datasets without negative ratings.
Preference Prediction - Calculates preference scores for users and items based on learned latent representations.
Implicit Feedback Modeling - Designed to handle implicit feedback using specialized loss functions for datasets lacking negative ratings.
Recommendation List Generators - Generates ranked lists of item suggestions by synthesizing interaction data and metadata.
Hybrid Feature Embeddings - Combines collaborative filtering with item and user metadata by summing their latent vectors.
Personalized Item Ranking - Calculates preference scores for specific user-item pairs to generate personalized rankings.
Hybrid Recommendation Training - Learns preferences by processing interaction matrices and metadata via stochastic gradient descent.
Recommendation Model Training - Optimizes model parameters using interaction data and specific loss functions to learn user behavior.
Model Performance Evaluators - Includes tools for evaluating model precision and accuracy by comparing suggestions against ground truth.
Implicit Feedback Optimization - Trains models on positive-only interaction datasets using specialized losses like WARP or BPR.
Top-K Accuracy Evaluators - Measures recommendation accuracy by evaluating the proportion of relevant items in the top-k results.
Recommendation Libraries - Provides a comprehensive Python library for training and evaluating recommendation models.
User Behavior - Learns patterns from interaction matrices and metadata to predict future user behavior.
Stochastic Gradient Descent - Utilizes stochastic gradient descent to iteratively update model weights based on interaction samples.
Personalized Ranking Optimizers - Implements Bayesian Personalized Ranking to maximize the probability of user preference for interacted items.
Recommender Frameworks - Python implementation of collaborative and content-based learning-to-rank algorithms.
Recommender Systems - Hybrid recommendation algorithms for implicit and explicit feedback.

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lystlightfm

Features

Open-source alternatives to Lightfm

NicolasHug/Surprise

gorse-io/gorse

recommenders-team/recommenders

RUCAIBox/RecBole

Star history

Open-source alternatives to Lightfm

NicolasHug/Surprise

gorse-io/gorse

recommenders-team/recommenders

RUCAIBox/RecBole