Iskandar-W

Iskandar-W's Stars

• InternLM/InternLM

  Official release of InternLM2.5 7B base and chat models. 1M context support

  Language:Python5.8k418

• Litianyu141/Finite-Volume-Graph-Network

  Modified Meshgraphnets with more features

  Language:Python257

• inboxpraveen/movie-recommendation-system

  Movie Recommendation System with Complete End-to-End Pipeline, Model Intregration & Web Application Hosted. It will also help you build similar projects.

  Language:Jupyter Notebook8742

• rexrex9/kb4recMovielensDataProcess

  用来处理freebase, kb4rec, movielens它们数据集的项目

  Language:Python3017

• InternLM/Tutorial

  LLM Tutorial

  1k428

• mishafoniakov/multimodal_recommendation

  Application of multimodal models for the image recommendation systems

  Language:Jupyter Notebook1

• enoche/MultimodalRecSys

  A curated list of awesome resources about multimodal recommender systems.

  25719

• atharva1707/Movie-recommendation-system-using-Knowledge-graphs

  Language:Jupyter Notebook1

• TianJH2099/CHKGAN

  Collaborative Heterogeneous Knowledge Graph Attention Network for Recommendation System

  Language:Python2

• arnavc1712/RecNet

  Session based recommendation system using state of the art Transformers and Matrix Factorization on MovieLens-1M.

  Language:Jupyter Notebook62

• hibbanbutt/movie-recommender

  A movie recommendation app using Data Mining principles. Based on the MovieLens 1M dataset.

  Language:Jupyter Notebook1

• rposhala/Recommender-System-on-MovieLens-dataset

  Knowledge-based, Content-based and Collaborative Recommender systems are built on MovieLens dataset with 100,000 movie ratings. These Recommender systems were built using Pandas operations and by fitting KNN, SVD & deep learning models which use NLP techniques and NN architecture to suggest movies for the users based on similar users and for queries specific to genre, user, movie, rating, popularity.

  Language:Jupyter Notebook10521

• TaoMiner/joint-kg-recommender

  Language:Python23956

• tobiaswahyudi/GDP-MovieLens-Preprocessing

  Knowledge Graph Preprocessing from the MovieLens movie rating data, intersected with semantic information pulled from DBPedia.

  Language:Jupyter Notebook32

• houchenll/recommend-origin

  all about recommendation

  1

• tkcsdvd/neo4j-movielens

  Load MovieLens dataset into Neo4j

  Language:Python115

• GavinGrayer/myspark-movie

  MovieLens电影数据分析

  Language:Scala2

• lokiidokii/Movie-Recommendation-App

  Goes through the MovieLens 25M data set, which contains movie reviews and ratings, creating a search engine that finds recommendations for movies based on input from the user.

  Language:Jupyter Notebook1

• chatchat-space/Langchain-Chatchat

  Langchain-Chatchat（原Langchain-ChatGLM, Qwen 与 Llama 等）基于 Langchain 与 ChatGLM 等语言模型的 RAG 与 Agent 应用 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM, Qwen and Llama) RAG and Agent app with langchain

  Language:TypeScript30k5.2k

• meta-llama/llama

  Inference code for Llama models

  Language:Python54.3k9.3k

• hu-dske/KGAT-SR

  The codes for ICTAI'21 paper "KGAT-SR: Knowledge-Enhanced Graph Attention Network for Session-based Recommendation".

  Language:Python9

• SuikaXhq/TKGAT

  Tag-aware Knowledge Graph Attention Network (Knowledge-Based Systems)

  Language:Jupyter Notebook31

• datawhalechina/whale-quant

  本项目为量化开源课程，可以帮助人们快速掌握量化金融知识以及使用Python进行量化开发的能力。

  Language:Jupyter Notebook44182

• krahets/hello-algo

  《Hello 算法》：动画图解、一键运行的数据结构与算法教程。支持 Python, Java, C++, C, C#, JS, Go, Swift, Rust, Ruby, Kotlin, TS, Dart 代码。简体版和繁体版同步更新，English version ongoing

  Language:Java87.7k11.1k

• datawhalechina/competition-baseline

  数据挖掘、计算机视觉、自然语言处理、推荐系统竞赛知识、代码、思路

  Language:Jupyter Notebook4.1k1.1k

• hongleizhang/RSPapers

  A Curated List of Must-read Papers on Recommender System.

  6k1.3k