Papers Sequential Recommendation
“Sequential Recommendation” 태그가 달린 논문 663편 · 필터 해제
IntBMoE: Integrating Block-Level Conditioning into Expert Composition for Full-Participation Mixture-of-Experts
Mixture-of-Experts (MoE) scales capacity, but existing designs cannot set three quantities independently. For a single token, participation is how many experts contribute knowledge to its output, execution is how many ar…
Sequential RecommendationImage ClassificationCRAMER: Control via Request-Aware Masking for Editing Recommenders
Sequential recommendation models, while powerful, have limited flexibility in responding to immediate user requests, making it difficult to adapt their recommendations to the user's timely interests. Unfortunately, exist…
Sequential RecommendationPrompt EngineeringAdapting Knowledge Graphs for Behavior Denoising in Sequential Recommendation
Sequential recommendation predicts the next item from a user's interaction history, but not every interaction is equally informative. Real logs combine persistent preferences with temporary needs, exploration, and incide…
Sequential RecommendationKnowledge GraphsRecPFN: Prior-Fitted Networks for In-Context-Based Recommendations
We introduce RecPFN, a prior-fitted network that brings in-context learning to sequential recommendation. RecPFN is pretrained entirely on synthetic clickstream environments sampled from a broad structural causal prior, …
Sequential RecommendationWhen Machines Speak: A Unified Generative Framework for Integrating Machine-Native Symbols into Pretrained Large Language Models
Many real-world AI systems represent entities, behaviors, and structured information using discrete machine-native symbols rather than natural language. While these representations are compact and preserve task-relevant …
Sequential RecommendationStructured PredictionGOD: Enhancing Generalization via Deep Grafting for Sequential Recommendation
Sequential recommenders often struggle with sparse and noisy histories, limiting generalization to unseen interactions. Knowledge distillation mitigates this by transferring dense supervision from a teacher to a student.…
Sequential RecommendationKnowledge DistillationClockRoPE: Random Fourier Rotations for Temporal Routine Modeling
Rotary Position Embedding (RoPE) has been widely adopted in transformer-based large language models. However, its log-linear frequency schedule, originally designed to produce long-term attention decay, limits its adopti…
Sequential RecommendationSharpness-aware Model Merging with Salience Recovery for LLM-based Cross-Domain Sequential Recommendation
LLM-based Cross-Domain Sequential Recommendation (CDSR) leverages LLMs to enhance target performance via deep semantic reasoning, alleviating the dependency on overlapping users. Among LLM-based paradigms, model merging …
Sequential RecommendationREPREC: Representation Driven Parameter-Efficient Recommendation System
Large language models (LLMs) have been applied to sequential recommendation by incorporating collaborative signals through input conditioning or model adaptation. However, existing approaches often require LLM fine-tunin…
Sequential RecommendationComputational EfficiencyZero-Observation User Reactivation with Gap-Driven Dimensional Gating
Sequential recommendation (SR) models capture continuously observed behavior, but a returning user may have no interactions for months or years. We define this setting as Zero-Observation Reactivation: the user has a pre…
Sequential RecommendationRecRec: Recursive Refinement for Sequential Recommendation
Sequential recommender systems typically infer user preferences through single-pass encoding of interaction histories without iterative refinement, relying on increasingly deep architectures to capture complex patterns. …
Sequential RecommendationStructuring and Tokenizing Distributed User Interest Context for Generative Recommendation
Generative recommendation is an emerging paradigm that has shown promise in industrial recommendation systems, aiming to predict users' next interactions from their historical behaviors. At the core of generative recomme…
Sequential RecommendationRecommendation SystemsGenerative Archetype-Grounded Item Representations for Sequential Recommendation
Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a critical bottleneck. While pre-trained l…
Sequential RecommendationBAHSD: Bridging the Long-tail Gap via Adaptive Distillation in Black-box Sequential Recommendation
Sequential recommendation systems are widely adopted but often deployed as black-box APIs, which has driven recent interest in model extraction to replicate their capabilities locally. However, the long-tail distribution…
Sequential RecommendationContrastive LearningModel extractionMultimodal Music Recommendation System using LLMs
Music recommendation systems typically treat songs as opaque tokens, relying on collaborative interaction histories which overlooks semantic or acoustic content. Prior work has explored LLM-augmented, multimodal, and tex…
Sequential RecommendationRecommendation SystemsF-GRPO: Factorized Group-Relative Policy Optimization for Unified Candidate Generation and Ranking
Traditional retrieval pipelines optimize utility through stages of candidate retrieval and reranking, where ranking operates over a predefined candidate set. Large Language Models (LLMs) broaden this into a generative pr…
Multi-hop Question AnsweringSequential RecommendationPrecision Tracked Transformer via Kalman Filtering, Kriging and Process Noise
The Transformer is the foundational building block of modern AI, yet offers no principled handling of \emph{uncertainty}, which is prevalent in real applications: cold-start tokens with sparse histories in sequential rec…
Sequential RecommendationQuestion AnsweringAn Embarrassingly Simple Graph Heuristic Reveals Shortcut-Solvable Benchmarks for Sequential Recommendation
Sequential recommendation has increasingly shifted toward generative recommenders that combine sequential patterns with semantic item information. Yet these methods are often evaluated on a small set of widely used bench…
Sequential RecommendationBridging Textual Profiles and Latent User Embeddings for Personalization
Personalized systems rely on user representations to connect behavioral history with downstream recommendation applications. Existing methods typically employ either supervised latent user embeddings, which are effective…
Sequential RecommendationReinforcement LearningQuestion AnsweringPACEvolve++: Improving Test-time Learning for Evolutionary Search Agents
Large language models have become drivers of evolutionary search, but most systems rely on a fixed, prompt-elicited policy to sample next candidates. This limits adaptation in practical engineering and research tasks, wh…
Sequential RecommendationReinforcement Learning