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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

Showing 131140 of 554 papers

TitleStatusHype
Enhancing Sequential Music Recommendation with Personalized Popularity AwarenessCode0
Laser: Parameter-Efficient LLM Bi-Tuning for Sequential Recommendation with Collaborative Information0
SSD4Rec: A Structured State Space Duality Model for Efficient Sequential RecommendationCode1
MARS: Matching Attribute-aware Representations for Text-based Sequential RecommendationCode1
Bridging User Dynamics: Transforming Sequential Recommendations with Schrödinger Bridge and Diffusion Models0
Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session RecommendationCode1
Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item PredictionCode0
Are LLM-based Recommenders Already the Best? Simple Scaled Cross-entropy Unleashes the Potential of Traditional Sequential RecommendersCode0
CSRec: Rethinking Sequential Recommendation from A Causal PerspectiveCode0
DimeRec: A Unified Framework for Enhanced Sequential Recommendation via Generative Diffusion Models0
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