{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T10:40:26Z","timestamp":1741171226983,"version":"3.38.0"},"reference-count":34,"publisher":"National Library of Serbia","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2025]]},"abstract":"<jats:p>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. 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. 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.<\/jats:p>","DOI":"10.2298\/csis231101013l","type":"journal-article","created":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T09:12:41Z","timestamp":1738833161000},"page":"345-368","source":"Crossref","is-referenced-by-count":0,"title":["Improved session recommendation using contrastive learning based tail adjusted repeat aware graph neural network"],"prefix":"10.2298","volume":"22","author":[{"given":"Daifeng","family":"Li","sequence":"first","affiliation":[{"name":"School of Information Management, Sun Yat-sen University, Guangzhou, China"}]},{"given":"Tianjunzi","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Information Management, Nanjing University, Nanjing, China"}]},{"given":"Zhaohui","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Information Management, Sun Yat-sen University, Guangzhou, China"}]},{"given":"Xiaowen","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Information Management, Sun Yat-sen University, Guangzhou, China"}]},{"given":"Dingquan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Management, Sun Yat-sen University, Guangzhou, China"}]},{"given":"Andrew","family":"Madden","sequence":"additional","affiliation":[{"name":"University of Sheffield, South Yorkshire, England"}]}],"member":"1078","reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Benson, A., Kumar, R., Tomkins, A.: Modeling user consumption sequences. 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