Papers with Code 아카이브

Marketing

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

0

평가표

1

보존된 평가 기록

1

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/www2022paper/www-2022-paper-supplementary-materials

공식 코드

LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm

https://github.com/xfold/NormativeApproachToDiscrimination

공식 코드

A Normative approach to Attest Digital Discrimination

https://github.com/xiufanyu/gct

공식 코드

Generalized Causal Tree for Uplift Modeling

https://github.com/xy2119/Causal_Knowledge_GNN

공식 코드

Uplift Modeling based on Graph Neural Network Combined with Causal Knowledge

https://github.com/yanlingpan/chatgpt_sentiment

공식 코드

Public Attitudes Toward ChatGPT on Twitter: Sentiments, Topics, and Occupations

https://github.com/yeaso/chinese-affective-computing-dataset

공식 코드

A Chinese Multi-label Affective Computing Dataset Based on Social Media Network Users

https://github.com/yixianqianzy/cgh

공식 코드

Collaborative Generative Hashing for Marketing and Fast Cold-start Recommendation

https://github.com/zhang-xiaokun/cohhn

공식 코드

Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation

https://github.com/zhiweiuu/secs

공식 코드

Concordance and the Smallest Covering Set of Preference Orderings

https://github.com/zhpinkman/llms-effect-on-linguistic-diversity

공식 코드

The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Models

https://github.com/AemikaChow/DATASOURCE

Brand > Logo: Visual Analysis of Fashion Brands

https://github.com/GregMurray30/MultiTouchAttribution

Deep Neural Net with Attention for Multi-channel Multi-touch Attribution

https://github.com/Hamiedamr/EmotionNet-Nano-An-Efficient-Deep-Convolutional-Neural-Network-Design-for-Real-time-Facial-Expressi

EmotionNet Nano: An Efficient Deep Convolutional Neural Network Design for Real-time Facial Expression Recognition

https://github.com/Humboldt-WI/supervised_randomization

Affordable Uplift: Supervised Randomization in Controlled Experiments

https://github.com/IBM/causallib

Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

https://github.com/Jiayun-Dong/vic

Variable Importance Clouds: A Way to Explore Variable Importance for the Set of Good Models

https://github.com/MaryanMorel/ConvSCCS

ConvSCCS: convolutional self-controlled case series model for lagged adverse event detection

https://github.com/Nth-iteration-labs/ofmlr

Maximum likelihood estimation of a finite mixture of logistic regression models in a continuous data stream

https://github.com/PaddlePaddle/PaddleRec/tree/master/models/multitask/metaheac

Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising

https://github.com/PopovicMilica/causal_nets

Deep Neural Networks for Estimation and Inference

https://github.com/PrashantRanjan09/Improved-Word-Embeddings

Improving the Accuracy of Pre-trained Word Embeddings for Sentiment Analysis

https://github.com/Xovee/casflow

A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances

https://github.com/Xovee/ccgl

A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances

https://github.com/Xovee/hdgnn

A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances

https://github.com/YukiDaSlayer316/R-Tutorial

Structure Learning of Probabilistic Graphical Models: A Comprehensive Survey

https://github.com/allenai/dolma

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

https://github.com/arjit3004/Recommended-System

Statistical Analysis on E-Commerce Reviews, with Sentiment Classification using Bidirectional Recurrent Neural Network (RNN)

https://github.com/avinashbarnwal/AFTXGBoostPaper

Survival regression with accelerated failure time model in XGBoost

https://github.com/bettyttytty/Thompson-Sampling-for-a-Fatigue-aware-Online-Recommendation-System

Dynamic Learning of Sequential Choice Bandit Problem under Marketing Fatigue

https://github.com/biomedsciai/causallib

Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

https://github.com/bramiozo/PubScience

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

https://github.com/carmilea/carmilea

Explaining Large Language Models Decisions Using Shapley Values

https://github.com/crystalxur/calibration-metric-hte

Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects

https://github.com/dntai/dntai_fg20_affwild2_challenges

Affective Expression Analysis in-the-wild using Multi-Task Temporal Statistical Deep Learning Model

https://github.com/dreamh1gh/awesome-srl

What do you MEME? Generating Explanations for Visual Semantic Role Labelling in Memes

https://github.com/ekolodyazhnay/test-ds

A Deep-Learning-Based Fashion Attributes Detection Model

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.