Starred repositories
A curated list of awesome resources about multimodal recommender systems.
A link to repositories with datasets useful for recommendation tasks
A link to frameworks useful for the evaluation and reproducibility of recorecommendation algorithms
scripts and baselines for Spider: Yale complex and cross-domain semantic parsing and text-to-SQL challenge
Survey: A collection of AWESOME papers and resources on the large language model (LLM) related recommender system topics.
[WSDM'2024 Oral] "LLMRec: Large Language Models with Graph Augmentation for Recommendation"
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.
OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender Systems
A Comparative Framework for Multimodal Recommender Systems
A curated list of Generative Recommender Systems (Paper & Code)
Graph Neural Networks for Recommender Systems
An up-to-date, comprehensive and flexible recommendation library
A Deep Learning Based Context-Aware Recommendation Library
A unified, comprehensive and efficient recommendation library
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Source code for TKDE 2018 "Heterogeneous information network embedding for recommendation"
An Open-Source Toolkit for Heterogeneous Information Network Embedding (HINE)
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
A curated list of awesome Recommender System (Books, Conferences, Researchers, Papers, Github Repositories, Useful Sites, Youtube Videos)
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.
(Python, R, C) Collective (multi-view/multi-way) matrix factorization, including cold-start functionality (recommender systems, imputation, dimensionality reduction)
Source code for our LBR paper "Closed-Form Models for Collaborative Filtering with Side-Information" published at RecSys 2020.