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Collaborative Filtering

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하위 태스크: 0
평가표: 4
보존된 평가 기록: 30

코드

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

https://github.com/xiangwang1223/neural_graph_collaborative_filtering

공식 코드

Neural Graph Collaborative Filtering

https://github.com/xindubawukong222/K-Adaptive-CF

공식 코드

Efficient Model-Based Collaborative Filtering with Fast Adaptive PCA

https://github.com/xiwenchao/conTS-TOIS-2021

공식 코드

Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users

https://github.com/xurong-liang/LEGCF

공식 코드

Lightweight Embeddings for Graph Collaborative Filtering

https://github.com/xyliugo/odmt

공식 코드

Online Distillation-enhanced Multi-modal Transformer for Sequential Recommendation

https://github.com/yang-song/GASR

공식 코드

Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization

https://github.com/yangliangwei/graphau

공식 코드

Graph-based Alignment and Uniformity for Recommendation

https://github.com/yaochenzhu/crag

공식 코드

Collaborative Retrieval for Large Language Model-based Conversational Recommender Systems

https://github.com/yasdel/benchmark_recllm_fairness

공식 코드

Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recency

https://github.com/yasdel/llm-recsys

공식 코드

A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)

https://github.com/yhhe2004/ssc-kdd

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Collaborative Filtering Meets Spectrum Shift: Connecting User-Item Interaction with Graph-Structured Side Information

https://github.com/yhhe2004/stair

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STAIR: Manipulating Collaborative and Multimodal Information for E-Commerce Recommendation

https://github.com/yimutianyang/sigir23-vgcl

공식 코드

Generative Contrastive Graph Learning for Recommendation

https://github.com/yoonseok312/gram

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GRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based Collaborative Filtering

https://github.com/ysh-1998/cowpirec

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Collaborative Word-based Pre-trained Item Representation for Transferable Recommendation

https://github.com/ysh-1998/lrd

공식 코드

Sequential Recommendation with Latent Relations based on Large Language Model

https://github.com/yshenaw/GF_CF

공식 코드

How Powerful is Graph Convolution for Recommendation?

https://github.com/yuhan1i/g-refer

공식 코드

G-Refer: Graph Retrieval-Augmented Large Language Model for Explainable Recommendation

https://github.com/yunxiaochen/matrixcompletion_mixeddata

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A Generalized Latent Factor Model Approach to Mixed-data Matrix Completion with Entrywise Consistency

https://github.com/yuyingzhao/simaug

공식 코드

SimAug: Enhancing Recommendation with Pretrained Language Models for Dense and Balanced Data Augmentation

https://github.com/zc-97/dpt

공식 코드

Denoising and Prompt-Tuning for Multi-Behavior Recommendation

https://github.com/zealscott/ecf

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Towards Explainable Collaborative Filtering with Taste Clusters Learning

https://github.com/zfan20/graphda

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Graph Collaborative Signals Denoising and Augmentation for Recommendation

https://github.com/zhao254014/clustergcf

공식 코드

Cluster-based Graph Collaborative Filtering

https://github.com/zheyu-chen/weightedgcl

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Squeeze and Excitation: A Weighted Graph Contrastive Learning for Collaborative Filtering

https://github.com/zhwzhong/dagf

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Deep Attentional Guided Image Filtering

https://github.com/zinux1998/dmicf

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Dual-View Disentangled Multi-Intent Learning for Enhanced Collaborative Filtering

https://github.com/zoulixin93/NICF

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Neural Interactive Collaborative Filtering

https://github.com/zyang1580/PDA

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Causal Intervention for Leveraging Popularity Bias in Recommendation

https://github.com/zzhin/ccfcrec

공식 코드

Contrastive Collaborative Filtering for Cold-Start Item Recommendation

https://github.com/zzmeei/adaptivegcl

공식 코드

Adaptive Graph Contrastive Learning for Recommendation

https://github.com/zzyuuuu/lightccf

공식 코드

Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering

https://github.com/3ZadeSSG/ContentBased-Movie-Recommendation-using-Sentiment-Analysis

Movie Recommendation System using Sentiment Analysis from Microblogging Data

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