Papers with Code 아카이브
Sequential Recommendation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Intent-Enhanced Data Augmentation for Sequential Recommendation
Multi-Behavioral Sequential Recommendation
Direct Preference Optimization for LLM-Enhanced Recommendation Systems
Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation Models
FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services
Multimodal Point-of-Interest Recommendation
LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation
Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item Catalogs
TTT4Rec: A Test-Time Training Approach for Rapid Adaption in Sequential Recommendation
Autoregressive Generation Strategies for Top-K Sequential Recommendations
Train Once, Deploy Anywhere: Matryoshka Representation Learning for Multimodal Recommendation
TiM4Rec: An Efficient Sequential Recommendation Model Based on Time-Aware Structured State Space Duality Model
Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation
Data Augmentation for Sequential Recommendation: A Survey
HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User Modeling
Measuring Recency Bias In Sequential Recommendation Systems
Multi-intent Aware Contrastive Learning for Sequential Recommendation
Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive Learning
DV-FSR: A Dual-View Target Attack Framework for Federated Sequential Recommendation
Generative Recommender with End-to-End Learnable Item Tokenization
Sequential Recommendation via Adaptive Robust Attention with Multi-dimensional Embeddings
Enhancing Sequential Music Recommendation with Personalized Popularity Awareness
Laser: Parameter-Efficient LLM Bi-Tuning for Sequential Recommendation with Collaborative Information
SSD4Rec: A Structured State Space Duality Model for Efficient Sequential Recommendation
MARS: Matching Attribute-aware Representations for Text-based Sequential Recommendation
Bridging User Dynamics: Transforming Sequential Recommendations with Schrödinger Bridge and Diffusion Models
Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation
Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item Prediction
Are LLM-based Recommenders Already the Best? Simple Scaled Cross-entropy Unleashes the Potential of Traditional Sequential Recommenders
CSRec: Rethinking Sequential Recommendation from A Causal Perspective
DimeRec: A Unified Framework for Enhanced Sequential Recommendation via Generative Diffusion Models
Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation
SIGMA: Selective Gated Mamba for Sequential Recommendation
Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation
Efficient Inference of Sub-Item Id-based Sequential Recommendation Models with Millions of Items
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.