A Reproducible Analysis of Sequential Recommender Systems
Sequential Recommender Systems (SRSs) have emerged as a highly efficient approach to recommendation systems. By leveraging sequential data, SRSs can identify temporal patterns in user behaviour, significantly improving recommendation accuracy and relevance. Ensuring the reproducibility of these mode...
Saved in:
Main Authors: | Filippo Betello, Antonio Purificato, Federico Siciliano, Giovanni Trappolini, Andrea Bacciu, Nicola Tonellotto, Fabrizio Silvestri |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2025-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10813337/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Exploring the Side-Information Fusion for Sequential Recommendation
by: Seunghwan Choi, et al.
Published: (2025-01-01) -
Deep Sequential Model for Anchor Recommendation on Live Streaming Platforms
by: Shuai Zhang, et al.
Published: (2021-09-01) -
Sequential recommendation based on contrast enhanced time-aware self-attention mechanism
by: YU Yang, et al.
Published: (2025-01-01) -
DualCFGL: dual-channel fusion global and local features for sequential recommendation
by: Shuxu Chen, et al.
Published: (2024-12-01) -
Empirical and Experimental Perspectives on Big Data in Recommendation Systems: A Comprehensive Survey
by: Kamal Taha, et al.
Published: (2024-09-01)