.png){kind=icon}
返回
Exploring global information for session-based recommendation
.png){kind=icon}
OA
AI
DOI：10.1016/j.patcog.2023.109911.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Session-based recommendation (SBR) aims to recommend items based on anonymous behavior sequences. However, most existing SBR approaches focus solely on the current session while neglecting the item-transition information from other sessions, which suffer from the inability of modeling the complicated item-transition. To address the limitations, we introduce global item-transition information to augment the modeling of item-transitions. Specifically, we first propose a basic GNN-based framework (BGNN), which solely uses session-level item-transition information. Based on BGNN, we propose a novel approach, called Session -based Recommendation with Global Information (SRGI), which infers the user preferences via fully exploring item-transitions over all sessions from two different perspectives: (i) Fusion-based Model (SRGI-FM), which recursively incorporates the neighbor embeddings of each node on global graph into the learning process of item representation; and (ii) Constrained-based Model (SRGI-CM), which treats the global-level information as a constraint to ensure the learned item embeddings are consistent with the global item-transition. Extensive experiments conducted on three popular benchmark datasets demonstrate that both SRGI-FM and SRGI-CM outperform the state-of-the-art methods.
Keyword:
Session-based recommendation
Graph neural network
Graph contrastive learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Patterns of sexual, bilateral and interpopulational variation in human femoral neck‐shaft anglesOAAI
OAAI分享收藏
MPCCL: Multiview predictive coding with contrastive learning for person re-identification.png)
PATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容