{"total":46,"rows":[{"id":"2202.02519","title":"Intent Contrastive Learning for Sequential Recommendation","created":"2022-02-05","cited_by_count":91,"area":null},{"id":"2205.00976","title":"Knowledge Graph Contrastive Learning for Recommendation","created":"2022-05-02","cited_by_count":70,"area":null},{"id":"2109.06349","title":"Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning","created":"2021-09-13","cited_by_count":46,"area":null},{"id":"2406.10450","title":"TokenRec: Learning to Tokenize ID for LLM-based Generative Recommendation","created":"2024-06-15","cited_by_count":35,"area":null},{"id":"2309.07602","title":"Turning Dross Into Gold Loss: is BERT4Rec really better than SASRec?","created":"2023-09-14","cited_by_count":30,"area":null},{"id":"2207.07483","title":"A Systematic Review and Replicability Study of BERT4Rec for Sequential Recommendation","created":"2022-07-15","cited_by_count":22,"area":null},{"id":"2310.18608","title":"Embedding in Recommender Systems: A Survey","created":"2023-10-28","cited_by_count":20,"area":null},{"id":"2209.00456","title":"ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation","created":"2022-08-27","cited_by_count":19,"area":null},{"id":"2305.04619","title":"Graph Masked Autoencoder for Sequential Recommendation","created":"2023-05-08","cited_by_count":19,"area":null},{"id":"2406.03085","title":"Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation","created":"2024-06-05","cited_by_count":19,"area":null},{"id":"2212.08262","title":"Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation","created":"2022-12-16","cited_by_count":16,"area":null},{"id":"2409.18721","title":"Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item Catalogs","created":"2024-09-27","cited_by_count":15,"area":null},{"id":"2111.08268","title":"Pre-training Graph Neural Network for Cross Domain Recommendation","created":"2021-11-16","cited_by_count":14,"area":null},{"id":"2305.18238","title":"Multi-behavior Self-supervised Learning for Recommendation","created":"2023-05-22","cited_by_count":11,"area":null},{"id":"2308.01737","title":"MAP: A Model-agnostic Pretraining Framework for Click-through Rate Prediction","created":"2023-08-03","cited_by_count":10,"area":null},{"id":"2401.05975","title":"End-to-end Learnable Clustering for Intent Learning in Recommendation","created":"2024-01-11","cited_by_count":10,"area":null},{"id":"2204.11562","title":"Determinantal Point Process Likelihoods for Sequential Recommendation","created":"2022-04-25","cited_by_count":7,"area":null},{"id":"2412.07585","title":"Scaling Sequential Recommendation Models with Transformers","created":"2024-12-10","cited_by_count":7,"area":null},{"id":"2109.11654","title":"Modeling Dynamic Attributes for Next Basket Recommendation","created":"2021-09-23","cited_by_count":6,"area":null},{"id":"2401.09057","title":"CrossVideo: Self-supervised Cross-modal Contrastive Learning for Point Cloud Video Understanding","created":"2024-01-17","cited_by_count":6,"area":null},{"id":"2404.03354","title":"A Comprehensive Survey on Self-Supervised Learning for Recommendation","created":"2024-04-04","cited_by_count":6,"area":null},{"id":"2203.15508","title":"Improving Contrastive Learning with Model Augmentation","created":"2022-03-25","cited_by_count":5,"area":null},{"id":"2312.14447","title":"On the Effectiveness of Unlearning in Session-Based Recommendation","created":"2023-12-22","cited_by_count":4,"area":null},{"id":"2406.18470","title":"UniRec: A Dual Enhancement of Uniformity and Frequency in Sequential Recommendations","created":"2024-06-26","cited_by_count":4,"area":null},{"id":"2412.07382","title":"Temporal Linear Item-Item Model for Sequential Recommendation","created":"2024-12-10","cited_by_count":4,"area":null},{"id":"2204.02011","title":"ELECRec: Training Sequential Recommenders as Discriminators","created":"2022-04-05","cited_by_count":3,"area":null},{"id":"2208.03645","title":"Generating Negative Samples for Sequential Recommendation","created":"2022-08-07","cited_by_count":3,"area":null},{"id":"2301.12197","title":"Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation","created":"2023-01-28","cited_by_count":3,"area":null},{"id":"2306.01266","title":"Self Contrastive Learning for Session-based Recommendation","created":"2023-06-02","cited_by_count":3,"area":null},{"id":"2304.05615","title":"Deep Stable Multi-Interest Learning for Out-of-distribution Sequential Recommendation","created":"2023-04-12","cited_by_count":2,"area":null},{"id":"2310.09706","title":"AdaptSSR: Pre-training User Model with Augmentation-Adaptive Self-Supervised Ranking","created":"2023-10-15","cited_by_count":2,"area":null},{"id":"2509.13648","title":"Sequential Data Augmentation for Generative Recommendation","created":"2025-09-17","cited_by_count":2,"area":null},{"id":"2512.03067","title":"Quantifying the Potential to Escape Filter Bubbles: A Behavior-Aware Measure via Contrastive Simulation","created":"2025-11-27","cited_by_count":2,"area":null},{"id":"2512.17277","title":"Warmer for Less: A Cost-Efficient Strategy for Cold-Start Recommendations at Pinterest","created":"2025-12-19","cited_by_count":2,"area":null},{"id":"2204.10128","title":"Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation","created":"2022-04-21","cited_by_count":1,"area":null},{"id":"2407.00082","title":"Adapting Job Recommendations to User Preference Drift with Behavioral-Semantic Fusion Learning","created":"2024-06-24","cited_by_count":1,"area":null},{"id":"2606.11023","title":"Generative Archetype-Grounded Item Representations for Sequential Recommendation","created":"2026-06-09","cited_by_count":1,"area":null},{"id":"2207.07910","title":"Improving Multi-Interest Network with Stable Learning","created":"2022-07-14","cited_by_count":0,"area":null},{"id":"2210.16928","title":"FELRec: Efficient Handling of Item Cold-Start With Dynamic Representation Learning in Recommender Systems","created":"2022-10-30","cited_by_count":0,"area":null},{"id":"2404.18214","title":"Contrastive Learning Method for Sequential Recommendation based on Multi-Intention Disentanglement","created":"2024-04-28","cited_by_count":0,"area":null},{"id":"2405.18068","title":"A Survey of Latent Factor Models in Recommender Systems","created":"2024-05-28","cited_by_count":0,"area":null},{"id":"2409.08733","title":"Multi-intent Aware Contrastive Learning for Sequential Recommendation","created":"2024-09-13","cited_by_count":0,"area":null},{"id":"2604.14114","title":"ID and Graph View Contrastive Learning with Multi-View Attention Fusion for Sequential Recommendation","created":"2026-04-15","cited_by_count":0,"area":null},{"id":"2605.04723","title":"Rethinking Convolutional Networks for Attribute-Aware Sequential Recommendation","created":"2026-05-06","cited_by_count":0,"area":null},{"id":"2609.01057","title":"User Representation via Cross Multi-source Behavior Pre-training for Mobile Games","created":"2026-09-01","cited_by_count":0,"area":null},{"id":"2609.28972","title":"Cross-Country Code-Mixing for Generative Recommendation","created":"2026-09-24","cited_by_count":0,"area":null}],"next_cursor":null}