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https://github.com/wubinzzu/sequential-recommendation-papers
https://github.com/wubinzzu/sequential-recommendation-papers
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- Host: GitHub
- URL: https://github.com/wubinzzu/sequential-recommendation-papers
- Owner: wubinzzu
- Created: 2024-03-06T14:07:37.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-03-19T12:57:35.000Z (10 months ago)
- Last Synced: 2024-03-19T13:58:26.499Z (10 months ago)
- Size: 37.1 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Recommendation Systems
================
- [Category](#Category)
- [Sequential Recommendation](#static-negative-sampling)
Category
----
### Sequential Recommendation
- `SIGIR(2023)`Poisoning Self-supervised Learning Based Sequential Recommendations **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3539618.3591751)]** **[[Code](https://github.com/CongGroup/Poisoning-SSL-based-RS)]**
- `SIGIR(2023)`Knowledge-enhanced Multi-View Graph Neural Networks for Session-based Recommendation**[[PDF](https://dl.acm.org/doi/abs/10.1145/3539618.3591706)]**
- `SIGIR(2023)`MELT: Mutual Enhancement of Long-Tailed User and Item for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2304.08382.pdf)]** **[[Code](https://github.com/rlqja1107/MELT)]**
- `SIGIR(2023)`Distributionally Robust Sequential Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3539618.3591668)]** **[[Code]( https://anonymousrsr.github.io/RSR/)]**
- `SIGIR(2023)`Linear Attention Mechanism for Long-term Sequential Recommender Systems **[[PDF](https://dl.acm.org/doi/abs/10.1145/3539618.3591717)]** **[[Code](https://github.com/Applied-Machine-Learning-Lab/LinRec)]**
- `SIGIR(2023)`Graph Masked Autoencoder for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2305.04619.pdf)]** **[[Code](https://github.com/HKUDS/MAERec)]**
- `SIGIR(2023)`Meta-optimized Contrastive Learning for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2304.07763.pdf)]** **[[Code](https://github.com/QinHsiu/MCLRec)]**
- `SIGIR(2023)`Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3539618.3591672)]**
- `SIGIR(2023)`Frequency Enhanced Hybrid Attention Network for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2304.09184.pdf)]** **[[Code](https://github.com/sudaada/FEARec)]**
- `SIGIR(2023)`A Generic Learning Framework for Sequential Recommendation with Distribution Shifts **[[PDF](https://openreview.net/pdf?id=5vPaFVIrVLf)]** **[[Code](https://github.com/YangZhengyi98/DROS)]**
- `SIGIR(2023)`Multi-View Multi-Aspect Neural Networks for Next-Basket Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3539618.3591738)]** **[[Code](https://github.com/Hiiizhy/MMNR)]**
- `SIGIR(2023)`Next Basket Recommendation with Intent-aware Hypergraph Adversarial Network **[[PDF](https://dl.acm.org/doi/abs/10.1145/3539618.3591742)]**
- `KDD(2023)`Text Is All You Need: Learning Language Representations for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2305.13731.pdf)]**
- `KDD(2023)`Adaptive Disentangled Transformer for Sequential Recommendation **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3580305.3599253)]** **[[Code](https://github.com/defineZYP/ADT)]**
- `KDD(2023)`Contrastive Learning for User Sequence Representation in Personalized Product Search **[[PDF](http://playbigdata.ruc.edu.cn/dou/publication/2023_KDD_CLPS.pdf)]**
- `WWW(2023)`Enhancing Hierarchy-Aware Graph Networks with Deep Dual Clustering for Session-based Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3543507.3583247)]**
- `WWW(2023)`Joint Internal Multi-Interest Exploration and External Domain Alignment for Cross Domain Sequential Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3543507.3583366)]**
- `WWW(2023)`Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation **[[PDF](https://arxiv.org/pdf/2305.05848.pdf)]**
- `WWW(2023)`Dual-interest Factorization-heads Attention for Sequential Recommendation **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3543507.3583278)]** **[[Code](https://github.com/tsinghua-fib-lab/WWW2023-DFAR)]**
- `WWW(2023)`A Counterfactual Collaborative Session-based Recommender System **[[PDF](https://arxiv.org/pdf/2301.13364.pdf)]** **[[Code](https://github.com/wzsong17/COCO-SBRS)]**
- `WWW(2023)`Debiased Contrastive Learning for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2303.11780.pdf)]** **[[Code](https://github.com/HKUDS/DCRec)]**
- `WWW(2023)`MMMLP: Multi-modal Multilayer Perceptron for Sequential Recommendations **[[PDF](https://dl.acm.org/doi/abs/10.1145/3543507.3583378)]** **[[Code](https://github.com/Applied-Machine-Learning-Lab/MMMLP)]**
- `WWW(2023)`AutoMLP: Automated MLP for Sequential Recommendations **[[PDF](https://arxiv.org/pdf/2303.06337.pdf)]**
- `WWW(2023)`Modeling Temporal Positive and Negative Excitation for Sequential Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3543507.3583463)]** **[[Code](https://1https//github.com/THUwangcy/ReChorus)]**
- `WWW(2023)`A Self-Correcting Sequential Recommender **[[PDF](https://arxiv.org/pdf/2303.02297.pdf)]** **[[Code](https://github.com/TempSDU/STEAM)]**
- `WWW(2023)`Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2301.12197.pdf)]** **[[Code](https://github.com/zfan20/MStein)]**
- `ICDE(2023)`Sequential Recommendation with User Causal Behavior Discovery **[[PDF](https://ieeexplore.ieee.org/document/10184783)]**
- `ICDE(2023)`Contrastive Enhanced Slide Filter Mixer for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2305.04322.pdf)]** **[[Code](https://github.com/sudaada/SLIME4Rec)]**
- `CIKM(2023)`AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2205.08776.pdf)]** **[[Code](https://github.com/juyongjiang/AdaMCT)]**
- `CIKM(2023)`AutoSeqRec: Autoencoder for Efficient Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2308.06878.pdf)]** **[[Code](https://github.com/sliu675/AutoSeqRec)]**
- `CIKM(2023)`Text Matching Improves Sequential Recommendation by Reducing Popularity Biases **[[PDF](https://arxiv.org/pdf/2308.14029.pdf)]** **[[Code](https://github.com/OpenMatch/TASTE)]**
- `CIKM(2023)`Adaptive Multi-Modalities Fusion in Sequential Recommendation Systems **[[PDF](https://arxiv.org/pdf/2308.15980.pdf)]** **[[Code](https://github.com/HoldenHu/MMSR)]**
- `WSDM(2023)`IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2208.04600.pdf)]**
- `WSDM(2023)`Multi-Intentions Oriented Contrastive Learning for Sequential Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3539597.3570411)]**
- `WSDM(2023)`DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation **[[PDF](https://arxiv.org/pdf/2210.16591.pdf)]** **[[Code](https://github.com/Yifang-Qin/DisenPOI)]**
- `WSDM(2023)`Exploiting Explicit and Implicit Item relationships for Session-based Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3539597.3570432)]** **[[Code](https://github.com/YuhanZhen/WSDM23-DGNNs--for-Session-based-Recommendation)]**
- `IJICA(2023)`Sequential Recommendation with Probabilistic Logical Reasoning **[[PDF](https://arxiv.org/pdf/2304.11383.pdf)]** **[[Code](https://github.com/Huanhuaneryuan/SR-PLR)]**
- `AAAI(2023)`Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation **[[PDF](https://ojs.aaai.org/index.php/AAAI/article/view/25540)]** **[[Code](https://github.com/KingGugu/TiCoSeRec)]**
- `ICLR(2023)`RESACT: Reinforcing Long-Term Engagement In Sequential Recommendation With Rscidual Actor **[[PDF](https://arxiv.org/pdf/2206.02620.pdf)]**
- `TNNLS(2023)`SMONAC: Supervised Multiobjective Negative Actor–Critic for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2206.02620.pdf)]**
- `TNNLS(2023)`G3SR: Global Graph Guided Session-Based Recommendation **[[PDF](https://arxiv.org/pdf/2206.02620.pdf)]**
- `TNNLS(2023)`GCRec: Graph-Augmented Capsule Network for Next-Item Recommendation
- `TNNLS(2023)`Conversation-Based Adaptive Relational Translation Method for Next POI Recommendation With Uncertain Check-Ins **[[PDF](https://ieeexplore.ieee.org/document/9712472)]** **[[Code](http://github.com/CART2020/CART)]**
- `TKDE(2023)`Graph-Based Embedding Smoothing for Sequential Recommendation **[[PDF](https://ieeexplore.ieee.org/abstract/document/9405450)]** **[[Code](http://github.com/zhuty16/GES)]**
- `TKDE(2023)`Incorporating Link Prediction into Multi-Relational Item Graph Modeling for Session-Based Recommendation **[[PDF](https://ieeexplore.ieee.org/abstract/document/9536374)]**
- `TKDE(2023)`CmnRec: Sequential Recommendations With Chunk-Accelerated Memory Network **[[PDF](https://dlnext.acm.org/doi/10.1109/TKDE.2022.3141102)]**
- `TKDE(2023)`M2: Mixed Models With Preferences, Popularities and Transitions for Next-Basket Recommendation **[[PDF](https://ieeexplore.ieee.org/document/9681238)]** **[[Code](https://github.com/ninglab/M2)]**
- `TKDE(2023)`Parallel Split-Join Networks for Shared Account Cross-Domain Sequential Recommendations **[[PDF](https://ieeexplore.ieee.org/document/9647967)]**
- `TKDE(2023)`Dynamic Graph Neural Networks for Sequential Recommendation **[[PDF](https://ieeexplore.ieee.org/abstract/document/9714053/)]**
- `TKDE(2023)`LOKI: A Practical Data Poisoning Attack Framework Against Next Item Recommendations **[[PDF](https://ieeexplore.ieee.org/abstract/document/9806383)]**
- `TKDE(2023)`Multi-Behavior Sequential Recommendation With Temporal Graph Transformer **[[PDF](https://ieeexplore.ieee.org/document/9774907/)]** **[[Code](https://github.com/akaxlh/TGT)]**
- `TKDE(2023)`Reinforcement Learning-Enhanced Shared-Account Cross-Domain Sequential Recommendation **[[PDF](https://ieeexplore.ieee.org/document/9802739)]**
- `TKDE(2023)`Disentangled Graph Neural Networks for Session-Based Recommendation **[[PDF](https://ieeexplore.ieee.org/abstract/document/9899738)]** **[[Code](https://github.com/AnsongLi/Disen-GNN)]**
- `TKDE(2023)`Multi Global Information Assisted Streaming Session-Based Recommendation System **[[PDF](https://ieeexplore.ieee.org/abstract/document/9858599/)]**
- `TKDE(2023)`Data Augmented Sequential Recommendation Based on Counterfactual Thinking **[[PDF](https://ieeexplore.ieee.org/document/9950302/)]**
- `TKDE(2023)`Feature-Level Deeper Self-Attention Network With Contrastive Learning for Sequential Recommendation **[[PDF](https://ieeexplore.ieee.org/abstract/document/10059216)]**
- `TKDE(2023)`Reinforcement Learning Based Path Exploration for Sequential Explainable Recommendation **[[PDF](https://ieeexplore.ieee.org/document/10018538/)]**
- `TOIS(2023)`Learning Dual-view User Representations for Enhanced Sequential Recommendation **[[PDF](https://dl.acm.org/doi/10.1145/3572028)]**
- `TOIS(2023)`Position-Enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations **[[PDF](https://dl.acm.org/doi/10.1145/3511700)]** **[[Code](https://github.com/drhuangliwei/PTGCN)]**
- `TOIS(2023)`Sequential Recommendation with Multiple Contrast Signals **[[PDF](https://dl.acm.org/doi/10.1145/3522673)]** **[[Code](https://github.com/THUwangcy/ReChorus/tree/TOIS22)]**
- `SIGIR(2022)`Decoupled Side Information Fusion for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2204.11046.pdf)]** **[[Code](https://github.com/AIM-SE/DIF-SR)]**
- `SIGIR(2022)`On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation**[[PDF](https://arxiv.org/pdf/2204.11091.pdf)]** **[[Code](https://github.com/xiaxin1998/OD-Rec)]**
- `SIGIR(2022)`Multi-Agent RL-based Information Selection Model for Sequential Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3477495.3532022)]**
- `SIGIR(2022)`An Attribute-Driven Mirroring Graph Network for Session-based Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3477495.3531935)]** **[[Code](https://github.com/WHUIR/MGS)]**
- `SIGIR(2022)`When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2205.01286.pdf)]** **[[Code](https://github.com/WHUIR/MGNM)]**
- `SIGIR(2022)`Price DOES Matter! Modeling Price and Interest Preferences in Session-based Recommendation **[[PDF](https://arxiv.org/pdf/2205.04181.pdf)]** **[[Code](https://github.com/Zhang-xiaokun/CoHHN)]**
- `SIGIR(2022)`AutoGSR: Neural Architecture Search for Graph-based Session Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3477495.3531940)]**
- `SIGIR(2022)`Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2205.10775.pdf)]** **[[Code](https://github.com/RUCAIBox/Ada-Ranker)]**
- `SIGIR(2022)`Multi-Faceted Global Item Relation Learning for Session-Based Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3477495.3532024)]** **[[Code](https://github.com/zc-97/MGIR)]**
- `SIGIR(2022)`ReCANet: A Repeat Consumption-Aware Neural Network for Next Basket Recommendation in Grocery Shopping **[[PDF](https://dl.acm.org/doi/abs/10.1145/3477495.3531708)]** **[[Code](https://github.com/mzhariann/recanet)]**
- `SIGIR(2022)`Determinantal Point Process Set Likelihood-Based Loss Functions for Sequential Recommendation **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3477495.3531965)]**
- `SIGIR(2022)`Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation **[[PDF](https://arxiv.org/pdf/2205.06058.pdf)]** **[[Code](https://github.com/summmeer/session-based-news-recommendation)]**
- `SIGIR(2022)`Coarse-to-Fine Sparse Sequential Recommendation **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3477495.3531732)]**
- `SIGIR(2022)`Dual Contrastive Network for Sequential Recommendation **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3477495.3531918)]**
- `SIGIR(2022)`Explainable Session-based Recommendation with Meta-Path Guided Instances and Self-Attention Mechanism **[[PDF](https://dl.acm.org/doi/abs/10.1145/3477495.3531895)]**
- `SIGIR(2022)`Item-Provider Co-learning for Sequential Recommendation **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3477495.3531756)]** **[[Code](https://github.com/siat-nlp/IPSRec)]**
- `SIGIR(2022)`RESETBERT4Rec: A Pre-training Model Integrating Time And User Historical Behavior for Sequential Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3477495.3532054)]**
- `SIGIR(2022)`Enhancing Hypergraph Neural Networks with Intent Disentanglement for Session-based Recommendation **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3477495.3531794)]**
- `SIGIR(2022)`CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space **[[PDF](https://arxiv.org/pdf/2204.11067.pdf)]** **[[Code](https://github.com/RUCAIBox/CORE)]**
- `SIGIR(2022)`DAGNN: Demand-aware Graph Neural Networks for Session-based Recommendation **[[PDF](https://www.academia.edu/download/82220302/2105.14428v1.pdf)]**
- `SIGIR(2022)`Progressive Self-Attention Network with Unsymmetrical Positional Encoding for Sequential Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3477495.3531800)]**
- `SIGIR(2022)`ELECRec: Training Sequential Recommenders as Discriminators **[[PDF](https://arxiv.org/pdf/2204.02011.pdf)]** **[[Code](https://github.com/YChen1993/ELECRec)]**
- `SIGIR(2022)`Exploiting Session Information in BERT-based Session-aware Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2204.10851.pdf)]** **[[Code](https://github.com/theeluwin/session-aware-bert4rec)]**
- `KDD(2022)`Towards Universal Sequence Representation Learning for Recommender Systems **[[PDF](https://arxiv.org/pdf/2206.05941.pdf)]** **[[Code](https://github.com/RUCAIBox/UniSRec)]**
- `KDD(2022)`Multi-Behavior Hypergraph-Enhanced Transformer for Next-Item Recommendation **[[PDF](https://arxiv.org/pdf/2207.05584.pdf)]** **[[Code](https://github.com/yuh-yang/MBHT-KDD22)]**
- `KDD(2022)`Debiasing Learning for Membership Inference Attacks Against Recommender Systems **[[PDF](https://arxiv.org/pdf/2206.12401.pdf)]** **[[Code](https://github.com/WZH-NLP/DL-MIA-KDD-2022)]**
- `KDD(2022)`Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3534678.3539430)]** **[[Code](https://github.com/KhalilDMK/DebiasedBERT4Rec)]**
- `WWW(2022)`Disentangling Long and Short-Term Interests for Recommendation **[[PDF](https://arxiv.org/pdf/2202.13090.pdf)]** **[[Code](https://github.com/tsinghua-fib-lab/CLSR)]**
- `WWW(2022)`Filter-enhanced MLP is All You Need for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2202.13556.pdf)]** **[[Code](https://github.com/RUCAIBox/FMLP-Rec)]**
- `WWW(2022)`Intent Contrastive Learning for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2202.02519.pdf)]** **[[Code](https://github.com/salesforce/ICLRec)]**
- `WWW(2022)`Learn over Past, Evolve for Future: Search-based Time-aware Recommendation with Sequential Behavior Data **[[PDF](https://arxiv.org/pdf/2202.03097.pdf)]**
- `WWW(2022)`Sequential Recommendation via Stochastic Self-Attention **[[PDF](https://arxiv.org/pdf/2201.06035.pdf)]** **[[Code](https://github.com/zfan20/STOSA)]**
- `WWW(2022)`Deep Interest Highlight Network for Click-Through Rate Prediction in Trigger-Induced Recommendation **[[PDF](https://arxiv.org/pdf/2202.08959.pdf)]** **[[Code](https://github.com/EzailShen/WWW-22-DIHN)]**
- `ICDE(2022)`Contrastive Learning for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2010.14395.pdf)]** **[[Code](https://github.com/THUwangcy/ReChorus)]**
- `CIKM(2022)`Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest Sustainability **[[PDF](https://arxiv.org/pdf/2209.06644.pdf)]** **[[Code](https://github.com/dmhyun/PERIS)]**
- `CIKM(2022)`Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation **[[PDF](https://arxiv.org/pdf/2206.12779.pdf)]**
- `CIKM(2022)`Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3511808.3557289)]**
- `CIKM(2022)`Dual-Task Learning for Multi-Behavior Sequential Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3511808.3557298)]**
- `CIKM(2022)`Dually Enhanced Propensity Score Estimation in Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2303.08722.pdf)]**
- `CIKM(2022)`Temporal Contrastive Pre-Training for Sequential Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3511808.3557468)]**
- `CIKM(2022)`MAE4Rec: Storage-saving Transformer for Sequential Recommendations **[[PDF](https://dl.acm.org/doi/abs/10.1145/3511808.3557461)]**
- `CIKM(2022)`Time Lag Aware Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2208.04760.pdf)]**
- `CIKM(2022)`Hierarchical Item Inconsistency Signal learning for Sequence Denoising in Sequential Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3511808.3557348)]** **[[Code](https://github.com/zc-97/HSD)]**
- `CIKM(2022)`A Relevant and Diverse Retrieval-enhanced Data Augmentation Framework for Sequential Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3511808.3557071)]**
- `WSDM(2022)`Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation **[[PDF](https://arxiv.org/abs/2110.05730)]**
- `WSDM(2022)`Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce **[[PDF](https://arxiv.org/abs/2110.11072)]** **[[Code](https://github.com/urielsinger/Trans2D)]**
- `TNNLS(2022)`Time Interval-Enhanced Graph Neural Network for Shared-Account Cross-Domain Sequential Recommendation **[[PDF](https://arxiv.org/abs/2206.08050)]**
- `TNNLS(2022)`Neural Time-Aware Sequential Recommendation by Jointly Modeling Preference Dynamics and Explicit Feature Couplings **[[PDF](https://ieeexplore.ieee.org/document/9404857/)]**
- `TKDE(2022)`Modeling Dynamic Missingness of Implicit Feedback for Sequential Recommendation **[[PDF](https://ieeexplore.ieee.org/document/9035641)]**
- `TKDE(2022)`Personalized Long- and Short-term Preference Learning for Next POI Recommendation **[[PDF](https://ieeexplore.ieee.org/document/9117156/)]**
- `TKDE(2022)`Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation **[[PDF](https://dl.acm.org/doi/pdf/10.1609/aaai.v33i01.33015877)]**
- `TKDE(2022)`Personalized Graph Neural Networks With Attention Mechanism for Session-Aware Recommendation **[[PDF](https://ieeexplore.ieee.org/document/9226110/)]** **[[Code](https://github.com/CRIPAC-DIG/A-PGNN)]**
- `TKDE(2022)`HAM: Hybrid Associations Models for Sequential Recommendation **[[PDF](https://ieeexplore.ieee.org/document/10184628)]**
- `TKDE(2022)`Intention-Aware Sequential Recommendation With Structured Intent Transition **[[PDF](https://ieeexplore.ieee.org/document/9319534)]** **[[Code](https://github.com/lihy96/ISRec)]**
- `TKDE(2022)`Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation **[[PDF](https://ieeexplore.ieee.org/document/9319527)]**
- `TOIS(2022)`Graph Co-Attentive Session-based Recommendation **[[PDF](https://dl.acm.org/doi/10.1145/3486711)]**
- `TOIS(2022)`Collaborative Graph Learning for Session-based Recommendation **[[PDF](https://dl.acm.org/doi/10.1145/3490479)]**
- `TOIS(2022)`Sequential-Knowledge-Aware Next POI Recommendation: A Meta-Learning Approach **[[PDF](https://dl.acm.org/doi/10.1145/3460198)]**
- `TOIS(2022)`Learning from Substitutable and Complementary Relations for Graph-based Sequential
Product Recommendation **[[PDF](https://dl.acm.org/doi/10.1145/3464302)]**
- `TOIS(2022)`Learning to Learn a Cold-start Sequential Recommender **[[PDF](https://dl.acm.org/doi/10.1145/3466753)]**
- `TOIS(2022)`Exploiting Positional Information for Session-Based Recommendation **[[PDF](https://dl.acm.org/doi/10.1145/3473339)]**
- `TOIS(2022)`Learning a Hierarchical Intent Model for Next-Item Recommendation **[[PDF](https://dl.acm.org/doi/10.1145/3473972)]**
- `TOIS(2022)`CHA: Categorical Hierarchy-based Attention for Next POI Recommendation **[[PDF](https://dl.acm.org/doi/10.1145/3464300)]**
- `TOIS(2022)`Multi-interest Diversification for End-to-end Sequential Recommendation **[[PDF](https://dl.acm.org/doi/10.1145/3475768)]**
- `SIGIR(2021)`Category-aware Collaborative Sequential Recommendation **[[PDF](https://par.nsf.gov/servlets/purl/10279168)]** **[[Code](https://github.com/RenqinCai/CoCoRec)]**
- `SIGIR(2021)`Sequential Recommendation with Graph Convolutional Networks **[[PDF](https://arxiv.org/pdf/2106.14226.pdf)]** **[[Code](https://github.com/recommenders-team/recommenders)]**
- `SIGIR(2021)`StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking **[[PDF](https://arxiv.org/pdf/2012.07598.pdf)]** **[[Code](https://github.com/wangjiachun0426/StackRec)]**
- `SIGIR(2021)`Counterfactual Data-Augmented Sequential Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3404835.3462855)]**
- `SIGIR(2021)`CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2109.05261.pdf)]**
- `SIGIR(2021)`Dual Attention Transfer in Session-based Recommendation with Multi Dimensional Integration **[[PDF](https://dl.acm.org/doi/abs/10.1145/3404835.3462866)]**
- `SIGIR(2021)`Unsupervised Proxy Selection for Session-based Recommender Systems **[[PDF](https://arxiv.org/pdf/2107.03564.pdf)]**
- `SIGIR(2021)`The World is Binary: Contrastive Learning for Denoising Next Basket Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3404835.3462836)]**
- `KDD(2021)`SEMI: A Sequential Multi-Modal Information Transfer Network for E-Commerce Micro-Video Recommendations **[[PDF](https://dl.acm.org/doi/abs/10.1145/3447548.3467189)]**
- `WWW(2021)`Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2103.10693.pdf)]** **[[Code](https://github.com/ACVAE/ACVAE-PyTorch)]**
- `WWW(2021)`DeepRec: On-device Deep Learning for Privacy-Preserving Sequential Recommendation in Mobile Commerce **[[PDF](https://dl.acm.org/doi/abs/10.1145/3442381.3449942)]** **[[Code](https://github.com/hanjialiang/DeepRec)]**
- `WWW(2021)`Future-Aware Diverse Trends Framework for Recommendation **[[PDF](https://arxiv.org/pdf/2011.00422.pdf)]**
- `WWW(2021)`RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2101.12457.pdf)]**
- `WWW(2021)`Session-aware Linear Item-Item Models for Session-based Recommendation **[[PDF](https://arxiv.org/pdf/2103.16104.pdf)]** **[[Code](https://github.com/jin530/SLIST)]**
- `ICDE(2021)`Sequential Recommendation on Dynamic Heterogeneous Information Network **[[PDF](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9458797)]**
- `CIKM(2021)`Seq2Bubbles: Region-Based Embedding Learning for User Behaviors in Sequential Recommenders **[[PDF](https://dl.acm.org/doi/abs/10.1145/3459637.3482296)]**
- `CIKM(2021)`Enhancing User Interest Modeling with Knowledge-Enriched Itemsets for Sequential Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3459637.3482256)]**
- `CIKM(2021)`Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer **[[PDF](https://arxiv.org/pdf/2108.06625.pdf)]** **[[Code](https://github.com/DyGRec/TGSRec)]**
- `CIKM(2021)`Extracting Attentive Social Temporal Excitation for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2109.13539.pdf)]**
- `CIKM(2021)`Hyperbolic Hypergraphs for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2108.08134.pdf)]** **[[Code](https://github.com/Abigale001/h2seqrec)]**
- `CIKM(2021)`Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2109.11790.pdf)]** **[[Code](https://github.com/weizhangltt/dual-recommend)]**
- `CIKM(2021)`Lightweight Self-Attentive Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2108.11333.pdf)]**
- `CIKM(2021)`What is Next when Sequential Prediction Meets Implicitly Hard Interaction? **[[PDF](https://arxiv.org/pdf/2202.06620.pdf)]** **[[Code](https://github.com/hukx-issac/HAIL)]**
- `CIKM(2021)`Modeling Sequences as Distributions with Uncertainty for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2106.06165.pdf)]** **[[Code](https://github.com/DyGRec/DT4SR)]**
- `CIKM(2021)`Locker: Locally Constrained Self-Attentive Sequential Recommendation **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3459637.3482136)]** **[[Code](https://github.com/AaronHeee/LOCKER)]**
- `CIKM(2021)`CBML: A Cluster-based Meta-learning Model for Session-based Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3459637.3482239)]**
- `CIKM(2021)`Self-Supervised Graph Co-Training for Session-based Recommendation **[[PDF](https://arxiv.org/pdf/2108.10560.pdf)]** **[[Code](https://github.com/xiaxin1998/COTREC)]**
- `WSDM(2021)`Sparse-Interest Network for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2102.09267.pdf)]**
- `WSDM(2021)`An Efficient and Effective Framework for Session-based Social Recommendation **[[PDF](https://www.cse.ust.hk/~raywong/paper/wsdm21-SEFrame.pdf)]** **[[Code](https://github.com/twchen/SEFrame)]**
- `TOIS(2021)`Modeling Multiple Coexisting Category-Level Intentions for Next Item Recommendation **[[PDF](https://dl.acm.org/doi/10.1145/3441642)]**
- `TOIS(2021)`Interactive Sequential Basket Recommendation by Learning Basket Couplings and Positive/Negative Feedback **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3444368)]**
- `TOIS(2021)`Toward Dynamic User Intention: Temporal Evolutionary Effects of Item Relations in Sequential Recommendation **[[PDF](https://dl.acm.org/doi/10.1145/3432244)]**
- `TOIS(2021)`Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations **[[PDF](https://dl.acm.org/doi/10.1145/3426723)]**
- `SIGIR(2020)`Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation **[[PDF](https://arxiv.org/pdf/2006.06922.pdf)]** **[[Code](https://github.com/ciecus/MKM-SR)]**
- `SIGIR(2020)`GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation **[[PDF](https://arxiv.org/pdf/2007.02747.pdf)]**
- `SIGIR(2020)`Sequential Recommendation with Self-attentive Multi-adversarial Network **[[PDF](https://arxiv.org/pdf/2005.10602.pdf)]**
- `SIGIR(2020)`A General Network Compression Framework for Sequential Recommender Systems **[[PDF](https://arxiv.org/pdf/2004.13139.pdf)]** **[[Code](https://github.com/siat-nlp/CpRec)]**
- `SIGIR(2020)`Next-item Recommendation with Sequential Hypergraphs **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3397271.3401133)]**
- `SIGIR(2020)`KERL: A Knowledge-Guided Reinforcement Learning Model for Sequential Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3397271.3401134)]**
- `SIGIR(2020)`Time Matters: Sequential Recommendation with Complex Temporal Information **[[PDF](https://dl.acm.org/doi/abs/10.1145/3397271.3401154)]**
- `SIGIR(2020)`Modeling Personalized Item Frequency Information for Next-basket Recommendation **[[PDF](https://arxiv.org/pdf/2006.00556.pdf)]** **[[Code](https://github.com/HaojiHu/TIFUKNN)]**
- `KDD(2020)`Disentangled Self-Supervision in Sequential Recommenders **[[PDF](https://dl.acm.org/doi/abs/10.1145/3394486.3403091)]**
- `KDD(2020)`Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions **[[PDF](https://arxiv.org/pdf/2007.12986.pdf)]** **[[Code](https://github.com/spotify-research/RIPS_KDD2020)]**
- `KDD(2020)`Geography-Aware Sequential Location Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3394486.3403252)]** **[[Code](https://github.com/libertyeagle/GeoSAN)]**
- `KDD(2020)`Handling Information Loss of Graph Neural Networks for Session-based Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3394486.3403170)]** **[[Code](https://github.com/twchen/lessr)]**
- `KDD(2020)`On Sampling Top-K Recommendation Evaluation **[[PDF](https://arxiv.org/pdf/2106.10621.pdf)]** **[[Code](https://github.com/dli12/KDD20-On-Sampling-Top-K-Recommendation-Evaluation)]**
- `KDD(2020)`Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation **[[PDF](https://arxiv.org/pdf/2101.04849.pdf)]** **[[Code](https://github.com/huawei-noah/noah-research/tree/master/PMLAM)]**
- `WWW(2020)`Adaptive Hierarchical Translation-based Sequential Recommendation **[[PDF](https://people.engr.tamu.edu/caverlee/pubs/zhang20www.pdf)]**
- `WWW(2020)`Attentive Sequential Model of Latent Intent for Next Item Recommendation **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3366423.3380002)]**
- `WWW(2020)`Déjà vu: A Contextualized Temporal Attention Mechanism for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/2002.00741.pdf)]**
- `WWW(2020)`Intention Modeling from Ordered and Unordered Facets for Sequential Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3366423.3380190)]**
- `WWW(2020)`Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation **[[PDF](https://arxiv.org/pdf/1906.04473.pdf)]**
- `WWW(2020)`Keywords Generation Improves E-Commerce Session-based Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3366423.3380232)]** **[[Code](https://github.com/LeeeeoLiu/ESRM-KG)]**
- `WWW(2020)`Next Point-of-Interest Recommendation on Resource-Constrained Mobile Devices **[[PDF](https://dl.acm.org/doi/abs/10.1145/3366423.3380170)]**
- `ICDE(2020)`Toward Recommendation for Upskilling: Modeling Skill Improvement and Item Difficulty in Action Sequences **[[PDF](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9101667)]**
- `CIKM(2020)`Hybrid Sequential Recommender via Time-aware Attentive Memory Network **[[PDF](https://arxiv.org/pdf/2005.08598.pdf)]** **[[Code](https://github.com/cocoandpudding/MTAMRecommender)]**
- `CIKM(2020)`Improving End-to-End Sequential Recommendations with Intent-aware Diversification **[[PDF](https://arxiv.org/pdf/1908.10171.pdf)]**
- `CIKM(2020)`Quaternion-based self-Attentive Long Short-term User Preference Encoding for Recommendation **[[PDF](https://arxiv.org/pdf/2008.13335.pdf)]**
- `CIKM(2020)`Star Graph Neural Networks for Session-based Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3340531.3412014)]**
- `CIKM(2020)`S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization **[[PDF](https://arxiv.org/pdf/2008.07873.pdf)]** **[[Code](https://github.com/RUCAIBox/CIKM2020-S3Rec)]**
- `CIKM(2020)`DynamicRec: A Dynamic Convolutional Network for Next Item Recommendation **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3340531.3412118)]** **[[Code](https://github.com/Mehrab-Tanjim/DynamicRec)]**
- `WSDM(2020)`Sequential Modeling of Hierarchical User Intention and Preference for Next-item Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3336191.3371840)]**
- `WSDM(2020)`Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling **[[PDF](https://arxiv.org/pdf/1911.03883.pdf)]** **[[Code](https://github.com/qinjr/SCoRe)]**
- `WSDM(2020)`Time Interval Aware Self-Attention for Sequential Recommendation **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3336191.3371786)]**
- `WSDM(2020)`Time to Shop for Valentine's Day: Shopping Occasions and Sequential Recommendation in E-commerce **[[PDF](https://people.engr.tamu.edu/caverlee/pubs/wang20wsdm-valentine.pdf)]**
- `TKDE(2020)`MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation **[[PDF](https://ieeexplore.ieee.org/abstract/document/8534409)]** **[[Code](https://github.com/cuiqiang1990/MV-RNN)]**
- `TKDE(2020)`Translation-Based Sequential Recommendation for Complex Users on Sparse Data **[[PDF](https://ieeexplore.ieee.org/abstract/document/8669829)]**
- `TOIS(2020)`Exploiting Cross-session Information for Session-based Recommendation with Graph Neural Networks **[[PDF](https://dl.acm.org/doi/10.1145/3382764)]**
- `TOIS(2020)`Next-Item Recommendation via Collaborative Filtering with Bidirectional Item Similarity **[[PDF](https://dl.acm.org/doi/abs/10.1145/3366172)]**
- `SIGIR(2019)`A Collaborative Session-based Recommendation Approach with Parallel Memory Modules **[[PDF](https://dl.acm.org/doi/abs/10.1145/3331184.3331210)]** **[[Code](https://github.com/wmeirui/CSRM_SIGIR2019)]**
- `SIGIR(2019)`Gated Spectral Units: Modeling Co-evolving Patterns for Sequential Recommendation **[[PDF](https://dl.acm.org/doi/pdf/10.1145/3331184.3331329)]**
- `SIGIR(2019)`Sequence and Time Aware Neighborhood for Session-based Recommendations **[[PDF](https://dl.acm.org/doi/abs/10.1145/3331184.3331322)]** **[[Code](https://github.com/CRIPAC-DIG/SR-GNN)]**
- `KDD(2019)`Dual Sequential Prediction Models Linking Sequential Recommendation and Information Dissemination **[[PDF](https://dl.acm.org/doi/abs/10.1145/3292500.3330959)]** **[[Code](https://github.com/qitianwu/DEEMS-KDD-19)]**
- `KDD(2019)`Hierarchical Gating Networks for Sequential Recommendation **[[PDF](https://arxiv.org/pdf/1906.09217.pdf)]** **[[Code](https://github.com/allenjack/HGN)]**
- `KDD(2019)`Streaming Session-based Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3292500.3330839)]**
- `KDD(2019)`Sequential Scenario-Specific Meta Learner for Online Recommendation **[[PDF](https://arxiv.org/pdf/1906.00391.pdf)]** **[[Code](https://github.com/THUDM/ScenarioMeta)]**
- `WWW(2019)`TiSSA: A Time Slice Self-Attention Approach for Modeling Sequential User Behaviors **[[PDF](https://dl.acm.org/doi/abs/10.1145/3308558.3313495)]**
- `WWW(2019)`Context-Aware Sequential Recommendations withStacked Recurrent Neural Networks **[[PDF](https://dl.acm.org/doi/abs/10.1145/3308558.3313567)]**
- `WWW(2019)`Hierarchical Neural Variational Model for Personalized Sequential Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3308558.3313603)]**
- `WWW(2019)`Recurrent Convolutional Neural Network for Sequential Recommendation **[[PDF](https://dl.acm.org/doi/abs/10.1145/3308558.3313408)]**
- `WWW(2019)`Variational Session-based Recommendation Using Normalizing Flows **[[PDF](https://par.nsf.gov/servlets/purl/10122596)]**
- `WWW(2019)`R2SIGTP: a Novel Real-Time Recommendation System with Integration of Geography and Temporal Preference for Next Point-of-Interest **[[PDF](https://dl.acm.org/doi/abs/10.1145/3308558.3314120)]**
- `TOIS(2019)`Next and Next New POI Recommendation via Latent Behavior Pattern Inference **[[PDF](https://dl.acm.org/doi/10.1145/3354187)]**