Papers with Code 아카이브

Sequential Recommendation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 13
보존된 평가 기록: 20

Sequential recommendation is a sophisticated approach to providing personalized suggestions by analyzing users' historical interactions in a sequential manner. Unlike traditional recommendation systems, which consider items in isolation, sequential recommendation takes into account the temporal order of user actions. This method is particularly valuable in domains where the sequence of events matters, such as streaming services, e-commerce platforms, and social media.

코드

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

https://github.com/kinggugu/da-cl-4rec

공식 코드

Data Augmentation for Sequential Recommendation: A Survey

https://github.com/kinggugu/reppad

공식 코드

Repeated Padding for Sequential Recommendation

https://github.com/kinggugu/ticoserec

공식 코드

Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation

https://github.com/kinggugu/tta4sr

공식 코드

Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential Recommendation

https://github.com/krishnacharya/GLoSS

공식 코드

GLoSS: Generative Language Models with Semantic Search for Sequential Recommendation

https://github.com/krishnacharya/sequentialrec-fairness

공식 코드

Improving Minimax Group Fairness in Sequential Recommendation

https://github.com/kz-song/mmsrec

공식 코드

Self-Supervised Multi-Modal Sequential Recommendation

https://github.com/l-lyl/dpplikelihoods4seqrec

공식 코드

Determinantal Point Process Likelihoods for Sequential Recommendation

https://github.com/lachlanlin/pricdsr

공식 코드

Privacy-Preserving Cross-Domain Sequential Recommendation

https://github.com/lalunex/msdccl

공식 코드

Multi-Level Sequence Denoising with Cross-Signal Contrastive Learning for Sequential Recommendation

https://github.com/li-fangyu/sequential_rec

공식 코드

Improving Sequential Recommendation Models with an Enhanced Loss Function

https://github.com/libeibei95/sigma

공식 코드

Semantic Gaussian Mixture Variational Autoencoder for Sequential Recommendation

https://github.com/libertyeagle/LISA

공식 코드

Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation

https://github.com/linjc16/Rec-R1

공식 코드

Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement Learning

https://github.com/liuqidong07/LLM-ESR

공식 코드

LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation

https://github.com/liuqidong07/LLMEmb

공식 코드

LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation

https://github.com/liuqidong07/diffuasr

공식 코드

Diffusion Augmentation for Sequential Recommendation

https://github.com/ljy0ustc/llara

공식 코드

LLaRA: Large Language-Recommendation Assistant

https://github.com/ljy0ustc/patchrec

공식 코드

Multi-Grained Patch Training for Efficient LLM-based Recommendation

https://github.com/lmcRS/AWS-recommendation-papers

공식 코드

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

https://github.com/lpworld/DASL

공식 코드

Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction

https://github.com/lswhim/preferdiff

공식 코드

Preference Diffusion for Recommendation

https://github.com/lsx-uniwue/non-items-recbole

공식 코드

Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item Prediction

https://github.com/mamuyang/MIFN

공식 코드

Mixed Information Flow for Cross-domain Sequential Recommendations

https://github.com/margotyjx/CSRec_repo

공식 코드

CSRec: Rethinking Sequential Recommendation from A Causal Perspective

https://github.com/mercadolibre/srt

공식 코드

Scaling Sequential Recommendation Models with Transformers

https://github.com/mingyue-cheng/tssr

공식 코드

Empowering Sequential Recommendation from Collaborative Signals and Semantic Relatedness

https://github.com/mmsr23/mmsr

공식 코드

An Empirical Study of Training ID-Agnostic Multi-modal Sequential Recommenders

https://github.com/mtandhj/ce-sce-llmrec

공식 코드

Are LLM-based Recommenders Already the Best? Simple Scaled Cross-entropy Unleashes the Potential of Traditional Sequential Recommenders

https://github.com/nancheng58/debiasedsr_dro

공식 코드

Debiasing Sequential Recommenders through Distributionally Robust Optimization over System Exposure

https://github.com/nancheng58/recmamba

공식 코드

Uncovering Selective State Space Model's Capabilities in Lifelong Sequential Recommendation

https://github.com/nemo-1024/adrec

공식 코드

Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective Approach

https://github.com/netease-media/fparec

공식 코드

Learning Positional Attention for Sequential Recommendation

https://github.com/nguyenngocbaocmt02/fpd

공식 코드

Efficient Failure Pattern Identification of Predictive Algorithms

https://github.com/ninglab/ant

공식 코드

Multi-modality Meets Re-learning: Mitigating Negative Transfer in Sequential Recommendation

https://github.com/openmatch/taste

공식 코드

Text Matching Improves Sequential Recommendation by Reducing Popularity Biases

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