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Sequential Recommendation

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.

Papers

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Showing 101–125 of 554 papers

TitleStatusHype
Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation—0
SimRec: Mitigating the Cold-Start Problem in Sequential Recommendation by Integrating Item SimilarityCode1
Dual Conditional Diffusion Models for Sequential Recommendation—0
Modeling Temporal Positive and Negative Excitation for Sequential Recommendation—0
Pay Attention to Attention for Sequential Recommendation—0
Generative Diffusion Models for Sequential Recommendations—0
Cross-Domain Sequential Recommendation via Neural Process—0
Generate and Instantiate What You Prefer: Text-Guided Diffusion for Sequential Recommendation—0
Preference Diffusion for RecommendationCode1
Intent-Enhanced Data Augmentation for Sequential Recommendation—0
Multi-Behavioral Sequential RecommendationCode1
Evaluating Performance and Bias of Negative Sampling in Large-Scale Sequential Recommendation ModelsCode1
Direct Preference Optimization for LLM-Enhanced Recommendation Systems—0
FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services—0
Multimodal Point-of-Interest Recommendation—0
LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential RecommendationCode2
TTT4Rec: A Test-Time Training Approach for Rapid Adaption in Sequential RecommendationCode0
Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item CatalogsCode1
Autoregressive Generation Strategies for Top-K Sequential Recommendations—0
Train Once, Deploy Anywhere: Matryoshka Representation Learning for Multimodal RecommendationCode1
TiM4Rec: An Efficient Sequential Recommendation Model Based on Time-Aware Structured State Space Duality ModelCode1
Pre-trained Language Model and Knowledge Distillation for Lightweight Sequential Recommendation—0
Data Augmentation for Sequential Recommendation: A SurveyCode3
HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User ModelingCode4
Measuring Recency Bias In Sequential Recommendation Systems—0
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