jrzaurin / RecoTourView external linksLinks
A tour through recommendation algorithms in python [IN PROGRESS]
☆178Dec 26, 2024Updated last year
Alternatives and similar repositories for RecoTour
Users that are interested in RecoTour are comparing it to the libraries listed below
Sorting:
- Comparing keras, pytorch and gluon using neural collaborative filtering☆18Jun 28, 2019Updated 6 years ago
- A bit of everything about text and nlp [IN PROGRESS]☆28Nov 5, 2021Updated 4 years ago
- code for ResSys'18 paper: "Exploring Recommendations Under User-Controlled Data Filtering"☆23Oct 16, 2018Updated 7 years ago
- High-performance implementation of SLIM-based approaches for Top-N recommendation☆113Dec 24, 2021Updated 4 years ago
- Sample data science projects (machine learning, optimization, business intelligence)☆28Aug 12, 2018Updated 7 years ago
- Accompanying code for reproducing experiments from the HybridSVD paper. Preprint is available at https://arxiv.org/abs/1802.06398.☆25Jul 11, 2019Updated 6 years ago
- rectorch is a pytorch-based framework for state-of-the-art top-N recommendation☆150Mar 16, 2021Updated 4 years ago
- ☆21Nov 9, 2021Updated 4 years ago
- PyTorch Implementations For A Series Of Deep Learning-Based Recommendation Models☆310Nov 21, 2022Updated 3 years ago
- ☆10Jan 28, 2021Updated 5 years ago
- Taxi fare prediction using tensorflow probability☆15Jul 23, 2019Updated 6 years ago
- Example of orchestrating dependent Databricks jobs using Airflow☆11Dec 19, 2019Updated 6 years ago
- Deep recommender models using PyTorch.☆3,042Dec 21, 2022Updated 3 years ago
- Genetic Algorithm in Python, which could be used for Sampling, Feature Select, Model Select, etc in Machine Learning☆16Aug 19, 2019Updated 6 years ago
- Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec☆186Jan 27, 2020Updated 6 years ago
- A statistical framework for graph anomaly detection.☆17Sep 23, 2018Updated 7 years ago
- This is the official code used for WAT 2017 Description Paper titled A Bag of Useful Tricks for Practical Neural Machine Translation: Emb…☆12Oct 24, 2017Updated 8 years ago
- ☆12Sep 17, 2022Updated 3 years ago
- ☆15Aug 21, 2018Updated 7 years ago
- A small wrapper to do Beta Boosting with XgBoost☆15Oct 26, 2021Updated 4 years ago
- Code accompanying the paper 'Manifold MCMC methods for Bayesian inference in a wide class of diffusion models'☆10May 28, 2025Updated 8 months ago
- Code for "A Bilingual Generative Transformer for Semantic Sentence Embedding" published at EMNLP 2020.☆10Nov 20, 2020Updated 5 years ago
- Complementary-Similarity Learning using Quadruplet Network☆13Mar 2, 2020Updated 5 years ago
- NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization☆133May 5, 2022Updated 3 years ago
- Using Kafka-Python to illustrate a ML production pipeline☆112Dec 8, 2022Updated 3 years ago
- ☆51Jan 3, 2021Updated 5 years ago
- Top-level Conference Publications on Knowledge Graph☆342Aug 17, 2020Updated 5 years ago
- The hybrid model combining stacked denoising autoencoder with matrix factorization is applied, to predict the customer purchase behavior …☆73Aug 4, 2018Updated 7 years ago
- Model selection and stability plots☆12Apr 5, 2024Updated last year
- learn some Machine Learning algorithm with python☆13Jul 19, 2019Updated 6 years ago
- A Comparative Framework for Multimodal Recommender Systems☆1,018Dec 18, 2025Updated last month
- Codes for papers: 1. Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters (ICML). 2. Less is More: Explorin…☆78Aug 2, 2023Updated 2 years ago
- ☆15Jan 11, 2019Updated 7 years ago
- TalkingData AdTracking Fraud Detection Challenge on Kaggle Competition☆13Sep 24, 2018Updated 7 years ago
- ☆12Oct 19, 2020Updated 5 years ago
- ☆12Oct 18, 2020Updated 5 years ago
- Task-Guided Pair Embedding in Heterogeneous Network (CIKM 2019)☆12Aug 19, 2021Updated 4 years ago
- Next RecSys Library☆1,061Mar 24, 2023Updated 2 years ago
- 推荐、广告工业界经典以及最前沿的论文、资料集合/ Must-read Papers on Recommendation System and CTR Prediction☆1,021Jan 20, 2024Updated 2 years ago