Geodesic
Improved Session Recommendation Using Contrastive Learning based Tail Adjusted Repeat Aware Graph Neural Network
Computer Science and Information Systems, Tome 22 (2025) no. 1

Voir la notice de l'article provenant de la source Computer Science and Information Systems website

Purpose: Session-based recommendation using graph neural networks (GNN) is a popular approach to model users’ behaviors and attributes of items from the perspective of user-item interaction sequence. However, current researches seldom incorporate the unique attributes of items to delve into a comprehensive analysis of user behaviors. In addition, GNN faces three problems when encounting complex modeling scenarios: long-range dependencies, order information loss, and data sparsity, which are essential to modeling long-tail items. Methods: We study the interactions between users and items from a new perspective. A novel Contrastive Learning based Tail Adjusted Repeat Aware Graph Neural Network (CLTAR-GNN) is proposed to tackle the problems. A Tail Adjusted Repeat (TAR) mechanism captures users’ repeat-explore behaviors in both short-head and long-tail session items based on graph neural networks. Through the TAR, we are able to further understand the underlying graph-based mechanisms that influence user-item interactions. A Self-Attention (SA) network with position embedding is incorporated to overcome the sequence information loss issues, which may be caused by the complex user behaviors and item characteristics modeling. Finally, a mutli-task learning framework is employed to combine TAR, SA and a contrastive learning model into a unified framework to enhance model performance by collaboratively training graph and sequence-based embeddings. Results: Experimental results show that CLTAR-GNN outperforms the state-of-the-art session-based recommendation methods significantly. The average improvement compared with all baselines are 17.5% (HR@20) and 22.5% (MRR@20) on both experimental datasets.
Keywords: Session-based Recommendation, Contrastive Learning, Self-Attention Networks, Tail Adjusted Repeat 
@article{CSIS_2025_22_1_a16,
     author = {Daifeng Li and Tianjunzi Tian and Zhaohui Huang and Xiaowen Lin and Dingquan Chen and Andrew Madden},
     title = {Improved {Session} {Recommendation} {Using} {Contrastive} {Learning} based {Tail} {Adjusted} {Repeat} {Aware} {Graph} {Neural} {Network}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {22},
     number = {1},
     year = {2025},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2025_22_1_a16/}
}
TY  - JOUR
AU  - Daifeng Li
AU  - Tianjunzi Tian
AU  - Zhaohui Huang
AU  - Xiaowen Lin
AU  - Dingquan Chen
AU  - Andrew Madden
TI  - Improved Session Recommendation Using Contrastive Learning based Tail Adjusted Repeat Aware Graph Neural Network
JO  - Computer Science and Information Systems
PY  - 2025
VL  - 22
IS  - 1
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/CSIS_2025_22_1_a16/
ID  - CSIS_2025_22_1_a16
ER  - 
%0 Journal Article
%A Daifeng Li
%A Tianjunzi Tian
%A Zhaohui Huang
%A Xiaowen Lin
%A Dingquan Chen
%A Andrew Madden
%T Improved Session Recommendation Using Contrastive Learning based Tail Adjusted Repeat Aware Graph Neural Network
%J Computer Science and Information Systems
%D 2025
%V 22
%N 1
%I mathdoc
%U http://geodesic.mathdoc.fr/item/CSIS_2025_22_1_a16/
%F CSIS_2025_22_1_a16
Daifeng Li; Tianjunzi Tian; Zhaohui Huang; Xiaowen Lin; Dingquan Chen; Andrew Madden. Improved Session Recommendation Using Contrastive Learning based Tail Adjusted Repeat Aware Graph Neural Network. Computer Science and Information Systems, Tome 22 (2025) no. 1. http://geodesic.mathdoc.fr/item/CSIS_2025_22_1_a16/