Crab - A Python Library for Recommendation Engines This library intends to be a reference for recommendation engines in Python Programming language. It is written in pure python to maximize the cross-platform issue and exposes the recommendation logic to your application by easy to use API REST via web services. The library is extensible, so the user can create new representations, algorithms and the design is optimized for performance. It is also open-source so everyone can use it. If you want to see our plan release/roadmap, please take a look at our Issues Tracker: http://github.com/marcelcaraciolo/crab/issues
This library intends to be a reference for recommendation engines in Python
Overview
Knowledge-aware Coupled Graph Neural Network for Social Recommendation
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Attentive Social Recommendation: Towards User And Item Diversities
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Recommender systems are the systems that are designed to recommend things to the user based on many different factors
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Code for ICML2019 Paper "Compositional Invariance Constraints for Graph Embeddings"
Dependencies NOTE: This code has been updated, if you were using this repo earlier and experienced issues that was due to an outaded codebase. Please
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Mutual Fund Recommender System. Tailor for fund transactions.
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Jointly Learning Explainable Rules for Recommendation with Knowledge Graph
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Elliot is a comprehensive recommendation framework that analyzes the recommendation problem from the researcher's perspective.
Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
Code for MB-GMN, SIGIR 2021
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A Python implementation of LightFM, a hybrid recommendation algorithm.
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Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction
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Learning Fair Representations for Recommendation: A Graph-based Perspective, WWW2021
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A PyTorch implementation of "Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information" (WSDM 2021)
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Collaborative variational bandwidth auto-encoder (VBAE) for recommender systems.
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Graph Neural Network based Social Recommendation Model. SIGIR2019.
Basic Information: This code is released for the papers: Le Wu, Peijie Sun, Yanjie Fu, Richang Hong, Xiting Wang and Meng Wang. A Neural Influence Dif
This is our implementation of GHCF: Graph Heterogeneous Collaborative Filtering (AAAI 2021)
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A tensorflow implementation of the RecoGCN model in a CIKM'19 paper, titled with "Relation-Aware Graph Convolutional Networks for Agent-Initiated Social E-Commerce Recommendation".
This repo contains a tensorflow implementation of RecoGCN and the experiment dataset Running the RecoGCN model python train.py Example training outp