Item Cluster-aware Prompt Learning for Session-based Recommendation
Fuente:
arXiv
Saved in:
| Main Authors: | Yang, Wooseong, Wang, Chen, Song, Zihe, Zhang, Weizhi, Yu, Philip S. |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation
by: Zhang, Weizhi, et al.
Published: (2025)
by: Zhang, Weizhi, et al.
Published: (2025)
RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation
by: Li, Shijun, et al.
Published: (2026)
by: Li, Shijun, et al.
Published: (2026)
Mixed Supervised Graph Contrastive Learning for Recommendation
by: Zhang, Weizhi, et al.
Published: (2024)
by: Zhang, Weizhi, et al.
Published: (2024)
Session-based Recommender Systems: User Interest as a Stochastic Process in the Latent Space
by: Balcer, Klaudia, et al.
Published: (2025)
by: Balcer, Klaudia, et al.
Published: (2025)
Multi-Item-Query Attention for Stable Sequential Recommendation
by: Xu, Mingshi, et al.
Published: (2025)
by: Xu, Mingshi, et al.
Published: (2025)
Position-Aware Sequential Attention for Accurate Next Item Recommendations
by: Nabiev, Timur, et al.
Published: (2026)
by: Nabiev, Timur, et al.
Published: (2026)
Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation
by: Zhang, Weizhi, et al.
Published: (2024)
by: Zhang, Weizhi, et al.
Published: (2024)
Pareto Front Approximation for Multi-Objective Session-Based Recommender Systems
by: Wilm, Timo, et al.
Published: (2024)
by: Wilm, Timo, et al.
Published: (2024)
CADC: Encoding User-Item Interactions for Compressing Recommendation Model Training Data
by: Zarch, Hossein Entezari, et al.
Published: (2024)
by: Zarch, Hossein Entezari, et al.
Published: (2024)
Prompt Tuning for Item Cold-start Recommendation
by: Jiang, Yuezihan, et al.
Published: (2024)
by: Jiang, Yuezihan, et al.
Published: (2024)
Epistemic Uncertainty-aware Recommendation Systems via Bayesian Deep Ensemble Learning
by: Cheraghi, Radin, et al.
Published: (2025)
by: Cheraghi, Radin, et al.
Published: (2025)
Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions
by: Wilm, Timo, et al.
Published: (2023)
by: Wilm, Timo, et al.
Published: (2023)
Let It Go? Not Quite: Addressing Item Cold Start in Sequential Recommendations with Content-Based Initialization
by: Pembek, Anton, et al.
Published: (2025)
by: Pembek, Anton, et al.
Published: (2025)
PAP-REC: Personalized Automatic Prompt for Recommendation Language Model
by: Li, Zelong, et al.
Published: (2024)
by: Li, Zelong, et al.
Published: (2024)
Scaling Laws for Many-Shot In-Context Learning with Self-Generated Annotations
by: Gu, Zhengyao, et al.
Published: (2025)
by: Gu, Zhengyao, et al.
Published: (2025)
A Reproducibility Analysis of PO4ISR: Diagnosing and Mitigating Semantic Drift in LLM-Based Session Recommendation
by: Tiwari, Aditya, et al.
Published: (2026)
by: Tiwari, Aditya, et al.
Published: (2026)
Tokenize Once, Recommend Anywhere: Unified Item Tokenization for Multi-domain LLM-based Recommendation
by: Hou, Yu, et al.
Published: (2025)
by: Hou, Yu, et al.
Published: (2025)
AgentDR: Dynamic Recommendation with Implicit Item-Item Relations via LLM-based Agents
by: Yang, Mingdai, et al.
Published: (2025)
by: Yang, Mingdai, et al.
Published: (2025)
Lightweight yet Efficient: An External Attentive Graph Convolutional Network with Positional Prompts for Sequential Recommendation
by: Zhang, Jinyu, et al.
Published: (2025)
by: Zhang, Jinyu, et al.
Published: (2025)
Explainable Session-based Recommendation via Path Reasoning
by: Cao, Yang, et al.
Published: (2024)
by: Cao, Yang, et al.
Published: (2024)
SemSR: Semantics aware robust Session-based Recommendations
by: Narwariya, Jyoti, et al.
Published: (2025)
by: Narwariya, Jyoti, et al.
Published: (2025)
Rethinking ANN-based Retrieval: Multifaceted Learnable Index for Large-scale Recommendation System
by: Zhang, Jiang, et al.
Published: (2026)
by: Zhang, Jiang, et al.
Published: (2026)
Talos: Optimizing Top-$K$ Accuracy in Recommender Systems
by: Zhang, Shengjia, et al.
Published: (2026)
by: Zhang, Shengjia, et al.
Published: (2026)
Language-Model Prior Overcomes Cold-Start Items
by: Wang, Shiyu, et al.
Published: (2024)
by: Wang, Shiyu, et al.
Published: (2024)
Symmetric Graph Contrastive Learning against Noisy Views for Recommendation
by: Zhao, Chu, et al.
Published: (2024)
by: Zhao, Chu, et al.
Published: (2024)
DKINet: Medication Recommendation via Domain Knowledge Informed Deep Learning
by: Liu, Sicen, et al.
Published: (2023)
by: Liu, Sicen, et al.
Published: (2023)
BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Models
by: Yang, Weiqin, et al.
Published: (2026)
by: Yang, Weiqin, et al.
Published: (2026)
Enhancing Recommendation with Denoising Auxiliary Task
by: Liu, Pengsheng, et al.
Published: (2024)
by: Liu, Pengsheng, et al.
Published: (2024)
Learning to Collaborate via Structures: Cluster-Guided Item Alignment for Federated Recommendation
by: Tu, Yuchun, et al.
Published: (2026)
by: Tu, Yuchun, et al.
Published: (2026)
Modeling Behavioral Intensity and Transitions for Generative Recommendation
by: Yang, Wenxuan, et al.
Published: (2026)
by: Yang, Wenxuan, et al.
Published: (2026)
BPL: Bias-adaptive Preference Distillation Learning for Recommender System
by: Kang, SeongKu, et al.
Published: (2025)
by: Kang, SeongKu, et al.
Published: (2025)
Hierarchical Reinforcement Learning for Temporal Abstraction of Listwise Recommendation
by: Ji, Luo, et al.
Published: (2024)
by: Ji, Luo, et al.
Published: (2024)
An Extremely Data-efficient and Generative LLM-based Reinforcement Learning Agent for Recommenders
by: Feng, Shuang, et al.
Published: (2024)
by: Feng, Shuang, et al.
Published: (2024)
DiffGRM: Diffusion-based Generative Recommendation Model
by: Liu, Zhao, et al.
Published: (2025)
by: Liu, Zhao, et al.
Published: (2025)
DeGRe: Dense-supervised Generative Reranking for Recommendation
by: Song, Chaotian, et al.
Published: (2026)
by: Song, Chaotian, et al.
Published: (2026)
Breaking the Top-$K$ Barrier: Advancing Top-$K$ Ranking Metrics Optimization in Recommender Systems
by: Yang, Weiqin, et al.
Published: (2025)
by: Yang, Weiqin, et al.
Published: (2025)
LoopFM: Learning frOm HistOrical RePresentations of Foundation Model for Recommendation
by: Jiang, Shali, et al.
Published: (2026)
by: Jiang, Shali, et al.
Published: (2026)
A Knowledge Graph and Deep Learning-Based Semantic Recommendation Database System for Advertisement Retrieval and Personalization
by: Wang, Tangtang, et al.
Published: (2025)
by: Wang, Tangtang, et al.
Published: (2025)
ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction
by: Zou, Henry Peng, et al.
Published: (2024)
by: Zou, Henry Peng, et al.
Published: (2024)
Large Language Models for Next Point-of-Interest Recommendation
by: Li, Peibo, et al.
Published: (2024)
by: Li, Peibo, et al.
Published: (2024)
Similar Items
-
LLMInit: A Free Lunch from Large Language Models for Selective Initialization of Recommendation
by: Zhang, Weizhi, et al.
Published: (2025) -
RRCM: Ranking-Driven Retrieval over Collaborative and Meta Memories for LLM Recommendation
by: Li, Shijun, et al.
Published: (2026) -
Mixed Supervised Graph Contrastive Learning for Recommendation
by: Zhang, Weizhi, et al.
Published: (2024) -
Session-based Recommender Systems: User Interest as a Stochastic Process in the Latent Space
by: Balcer, Klaudia, et al.
Published: (2025) -
Multi-Item-Query Attention for Stable Sequential Recommendation
by: Xu, Mingshi, et al.
Published: (2025)