たまに追加される論文メモ
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Updated
Jun 9, 2024 - HTML
たまに追加される論文メモ
A configurable, tunable, and reproducible library for CTR prediction https://fuxictr.github.io
Anime, Manga, & Novel Recommender Website and Application - User and Content-Based Recommendation using Anilist Account (Android and Web). Recommends Anime, Manga (Manhwa, Manhua, One Shots), Novel (LN - Light Novel, WN - Web Novel).
Repository hosting code used to reproduce results in "Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations" (https://arxiv.org/abs/2402.17152, ICML'24).
pytorch version of neural collaborative filtering
"XRec: Large Language Models for Explainable Recommendation"
Tool for computing delta-hyperbolicity on distance matrix (point sets, graphs, etc.)
[WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"
[WWW'2024] "RLMRec: Representation Learning with Large Language Models for Recommendation"
"RecDiff: Diffusion Model for Social Recommendation"
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
This collection of neural networks, each designed for a different job. Whether it's recognizing images, understanding language, or tackling other cool tasks, I've got it covered. Dive in and see these neural networks in action, making complicated stuff look easy! 🚀🤖
A unified, comprehensive and efficient recommendation library
This is the repository to support the course in Recommender Systems
This repository features a recommendation system and analytics engine using datasets on users, organizations, contents, contacts, events, and recommendations. It includes data preprocessing, building a recommendation system, and creating visual reports with Power BI.
A simple Recommender System for well-being based on Genetic Algorithms.
Large scale recommender system inference Microservices and APIs (Dubbo 、gRPC and REST ) with Golang.
推荐系统入门指南,全面介绍了工业级推荐系统的理论知识(王树森推荐系统公开课-基于小红书的场景讲解工业界真实的推荐系统),如何基于TensorFlow2训练模型,如何实现高性能、高并发、高可用的Golang推理微服务。Comprehensively introduced the theory of industrial recommender system, how to trainning models based on TensorFlow2, how to implement the high-performance、high-concurrency and high-available inference services base on Golang.
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