A framework for large scale recommendation algorithms.
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Updated
Nov 15, 2024 - Python
A framework for large scale recommendation algorithms.
This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies.
CTR prediction models based on deep learning(基于深度学习的广告推荐CTR预估模型)
A Comparative Framework for Multimodal Recommender Systems
[WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"
CTR模型代码和学习笔记总结
RecTools - library to build Recommendation Systems easier and faster than ever before
A Comprehensive Framework for Building End-to-End Recommendation Systems with State-of-the-Art Models
Source code of CHAMELEON - A Deep Learning Meta-Architecture for News Recommender Systems
CORE is a plug-and-play conversational agent for any recommender system.
multi task mode for esmm and mmoe
机器学习、深度学习基础知识. 推荐系统及nlp相关算法实现
An easy-to-use for large scale recommendation algorithms.
A simple recommendation evaluation system, the algorithm includes SLIM, LFM, ItemCF, UserCF
practice on movielens using pytorch
Federated Neural Collaborative Filtering (FedNCF). Neural Collaborative Filtering utilizes the flexibility, complexity, and non-linearity of Neural Network to build a recommender system. Aim to federate this recommendation system.
Matrix Factorization reimplementation with pytorch
Multi-order Attentive Ranking Model for Sequential Recommendation
用户兴趣建模大赛 top10 开源代码
An example of doing MovieLens recommendations using triplet loss in Keras
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