alicogintel / Sdm
Sequential deep matching model for recommender system at Alibaba
Stars: ✭ 139
Programming Languages
python
139335 projects - #7 most used programming language
Projects that are alternatives of or similar to Sdm
Enmf
This is our implementation of ENMF: Efficient Neural Matrix Factorization (TOIS. 38, 2020). This also provides a fair evaluation of existing state-of-the-art recommendation models.
Stars: ✭ 96 (-30.94%)
Mutual labels: recommender-system
Tagrec
Towards A Standardized Tag Recommender Benchmarking Framework
Stars: ✭ 113 (-18.71%)
Mutual labels: recommender-system
Comirec
Source code and dataset for KDD 2020 paper "Controllable Multi-Interest Framework for Recommendation"
Stars: ✭ 123 (-11.51%)
Mutual labels: recommender-system
To Regex Range
Pass two numbers, get a regex-compatible source string for matching ranges. Fast compiler, optimized regex, and validated against more than 2.78 million test assertions. Useful for creating regular expressions to validate numbers, ranges, years, etc.
Stars: ✭ 97 (-30.22%)
Mutual labels: matching
Drl Rec
Deep reinforcement learning for recommendation system
Stars: ✭ 92 (-33.81%)
Mutual labels: recommender-system
Seldon Server
Machine Learning Platform and Recommendation Engine built on Kubernetes
Stars: ✭ 1,435 (+932.37%)
Mutual labels: recommender-system
Sigir2020 peterrec
Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation
Stars: ✭ 121 (-12.95%)
Mutual labels: recommender-system
Recsys
计算广告/推荐系统/机器学习(Machine Learning)/点击率(CTR)/转化率(CVR)预估/点击率预估
Stars: ✭ 1,350 (+871.22%)
Mutual labels: recommender-system
Movielens Recommender System Javascript
🍃 Recommender System in JavaScript for the MovieLens Database
Stars: ✭ 105 (-24.46%)
Mutual labels: recommender-system
Kddcup 2020
6th Solution for 2020-KDDCUP Debiasing Challenge
Stars: ✭ 118 (-15.11%)
Mutual labels: recommender-system
Flurs
🌊 FluRS: A Python library for streaming recommendation algorithms
Stars: ✭ 97 (-30.22%)
Mutual labels: recommender-system
Elasticctr
ElasticCTR，即飞桨弹性计算推荐系统，是基于Kubernetes的企业级推荐系统开源解决方案。该方案融合了百度业务场景下持续打磨的高精度CTR模型、飞桨开源框架的大规模分布式训练能力、工业级稀疏参数弹性调度服务，帮助用户在Kubernetes环境中一键完成推荐系统部署，具备高性能、工业级部署、端到端体验的特点，并且作为开源套件，满足二次深度开发的需求。
Stars: ✭ 123 (-11.51%)
Mutual labels: recommender-system
Ai Study
人工智能学习资料超全整理，包含机器学习基础ML、深度学习基础DL、计算机视觉CV、自然语言处理NLP、推荐系统、语音识别、图神经网路、算法工程师面试题
Stars: ✭ 93 (-33.09%)
Mutual labels: recommender-system
Bcpd
Bayesian Coherent Point Drift (BCPD/BCPD++); Source Code Available
Stars: ✭ 116 (-16.55%)
Mutual labels: matching
Rnn recsys
Our implementation of the paper "Embedding-based News Recommendation for Millions of Users"
Stars: ✭ 135 (-2.88%)
Mutual labels: recommender-system
Movielens Recommender
A pure Python implement of Collaborative Filtering based on MovieLens' dataset.
Stars: ✭ 131 (-5.76%)
Mutual labels: recommender-system
Rectorch
rectorch is a pytorch-based framework for state-of-the-art top-N recommendation
Stars: ✭ 121 (-12.95%)
Mutual labels: recommender-system
SDM: Sequential Deep Matching Model for Online Large-scale Recommender System
New Released Code!!!
Thanks to the DeepMatch Group members for providing doc and code.
Demo Code
Code (Python2.7, TF1.4) of the sequential deep matching (SDM) model for recommender system at Taobao. Current version only contains the core code of our model. The processes of data processing and evaluation are executed on our internal cloud platform ODPS.
Paper
Here is the arxiv link (accepted by CIKM 2019)
Citation:
@inproceedings{lv2019sdm,
title={SDM: Sequential deep matching model for online large-scale recommender system},
author={Lv, Fuyu and Jin, Taiwei and Yu, Changlong and Sun, Fei and Lin, Quan and Yang, Keping and Ng, Wilfred},
booktitle={Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
pages={2635--2643},
year={2019},
organization={ACM}
}
Dataset
JD Dataset: raw data, train and test data in the paper (tfrecord). The schema of raw data is shown in data/sample_data/.
Disclaimer
This is an implementation on experiment of offline JD dataset rather than the online official version. There may be differences between results reported in the paper and the released one, because the former one is achieved in distribution tensorflow on our internal deep learning platform PAI.
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].