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Sequential Recommendation

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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/ZM7/DGSR

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Dynamic Graph Neural Networks for Sequential Recommendation

https://github.com/agiresearch/idgenrec

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IDGenRec: LLM-RecSys Alignment with Textual ID Learning

https://github.com/agiresearch/openp5

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OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender Systems

https://github.com/ahmedrashed-ml/carca

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CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-Attention

https://github.com/aim-se/ac-tsr

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Attention Calibration for Transformer-based Sequential Recommendation

https://github.com/aim-se/da4rec

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Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation

https://github.com/akalikong/ilora

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Customizing Language Models with Instance-wise LoRA for Sequential Recommendation

https://github.com/akaxlh/tgt

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Multi-Behavior Sequential Recommendation with Temporal Graph Transformer

https://github.com/alicogintel/xdm

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XDM: Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender System

https://github.com/allenjack/HGN

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Hierarchical Gating Networks for Sequential Recommendation

https://github.com/alwaysfhao/tim4rec

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TiM4Rec: An Efficient Sequential Recommendation Model Based on Time-Aware Structured State Space Duality Model

https://github.com/amazon-science/sequential-recommendation-using-similarity

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SimRec: Mitigating the Cold-Start Problem in Sequential Recommendation by Integrating Item Similarity

https://github.com/anananan116/MBGen

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Multi-Behavior Generative Recommendation

https://github.com/apple/ml-negative-sampling

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Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation Models

https://github.com/applied-machine-learning-lab/dt4ier

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Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention

https://github.com/applied-machine-learning-lab/findrec

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FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation

https://github.com/applied-machine-learning-lab/kuaisim

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KuaiSim: A Comprehensive Simulator for Recommender Systems

https://github.com/applied-machine-learning-lab/llm-esr

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

https://github.com/applied-machine-learning-lab/llmemb

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

https://github.com/applied-machine-learning-lab/pad

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Pre-train, Align, and Disentangle: Empowering Sequential Recommendation with Large Language Models

https://github.com/asash/bert4rec_repro

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A Systematic Review and Replicability Study of BERT4Rec for Sequential Recommendation

https://github.com/asash/gptrec_rl

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Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement Learning

https://github.com/asash/gsasrec

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gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling

https://github.com/asash/recjpq

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RecJPQ: Training Large-Catalogue Sequential Recommenders

https://github.com/booml247/ctlsan

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C-TLSAN: Content-Enhanced Time-Aware Long- and Short-Term Attention Network for Personalized Recommendation

https://github.com/borealisai/sasrec-ccql

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Robust Reinforcement Learning Objectives for Sequential Recommender Systems

https://github.com/bytedance/hllm

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HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling

https://github.com/caop-kie/GenRec

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GenRec: Generative Sequential Recommendation with Large Language Models

https://github.com/cchao0116/CTSMA-ICML21

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Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation

https://github.com/cchao0116/EasyDGL

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EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning

https://github.com/cchao0116/S2PNM-TKDE2021

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Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation

https://github.com/chain123/recguru

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RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain Recommendation

https://github.com/chengkai-liu/RecBLR

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Behavior-Dependent Linear Recurrent Units for Efficient Sequential Recommendation

https://github.com/chengkai-liu/mamba4rec

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Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space Models

https://github.com/cheungdaven/DeepRec

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DeepRec: An Open-source Toolkit for Deep Learning based Recommendation

https://github.com/chongminggao/KuaiRand

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KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos

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