ple

Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

Recommendation Algorithm大规模推荐算法库，包含推荐系统经典及最新算法LR、Wide&Deep、DSSM、TDM、MIND、Word2Vec、Bert4Rec、DeepWalk、SSR、AITM，DSIN，SIGN，IPREC、GRU4Rec、Youtube_dnn、NCF、GNN、FM、FFM、DeepFM、DCN、DIN、DIEN、DLRM、MMOE、PLE、ESMM、ESCMM, MAML、xDeepFM、DeepFEFM、NFM、AFM、RALM、DMR、GateNet、NAML、DIFM、Deep Crossing、PNN、BST、AutoInt、FGCNN、FLEN、Fibinet、ListWise、DeepRec、ENSFM，TiSAS，AutoFI…

A PyTorch Library for Multi-Task Learning

MLGB is a library that includes many models of CTR Prediction & Recommender System by TensorFlow & PyTorch. 「妙计包」是一个包含50+点击率预估和推荐系统深度模型的、通过TensorFlow和PyTorch撰写的库。

练习下用pytorch来复现下经典的推荐系统模型, 如MF, FM, DeepConn, MMOE, PLE, DeepFM, NFM, DCN, AFM, AutoInt, ONN, FiBiNET, DCN-v2, AFN, DCAP等

DeepTab is a Python package that simplifies tabular deep learning by providing a suite of models for regression, classification, and distributional regression tasks. It includes models such as Mambular, TabM, FT-Transformer, TabulaRNN, TabTransformer, and tabular ResNets.

主流推荐系统Rank算法的实现

学习并复现经典的推荐系统多目标任务，如：SharedBottom、ESMM、MMoE、PLE

curriculum (laravel)

PreTab is a modular, scikit-learn-compatible representation and preprocessing library for tabular data. Its focus is feature representation and expansion: splines, neural basis maps, piecewise-linear encoding, kernel approximations, and language embeddings, each shipped as a standalone transformer that speaks the standard fit / transform.

Our contribution to Systems and Software Product Line Conference (SPLC) 2022 in Graz, Austria

Keras implementation of a DQN agent for solving OpenAI's Flappy Bird environment

A workaround for supporting UTF-8 in MultiCharts's PLE

A sequential recommendation system that predicts what users will watch next and how they'll rate it using SASRec and multi-task learning.

Reinforcement Learning project where Q-Learning and Neural Network agents learn to play Flappy Bird using PLE. Developed for Aprendizaje Automático II at UNR.

Multimodal multi-task short-video recommendation with PLE, MMoE, uncertainty weighting, PCGrad, gate analysis, and modality ablation.