.png){kind=icon}
返回
G∧3SR: Global Graph Guided Session-Based Recommendation
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2022.3159592.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Session-based recommendation tries to make use of anonymous session data to deliver high-quality recommendations under the condition that user profiles and the complete historical behavioral data of a target user are unavailable. Previous works consider each session individually and try to capture user interests within a session. Despite their encouraging results, these models can only perceive intra-session items and cannot draw upon the massive historical relational information. To solve this problem, we propose a novel method named global graph guided session-based recommendation (G<^>3SR). G<^>3SR decomposes the session-based recommendation workflow into two steps. First, a global graph is built upon all session data, from which the global item representations are learned in an unsupervised manner. Then, these representations are refined on session graphs under the graph networks, and a readout function is used to generate session representations for each session. Extensive experiments on two real-world benchmark datasets show remarkable and consistent improvements of the G<^>3SR method over the state-of-the-art methods, especially for cold items.
Keyword:
Data models
Computational modeling
Task analysis
Training
Recommender systems
Learning systems
Speech recognition
Graph networks
neural networks
recommender systems
session-based recommendation
unsupervised pre-training
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
机构
引用论文
.png){kind=icon}
分享收藏The way is the goal: how SecA transports proteins across the cytoplasmic membrane in bacteria目标是: SecA如何在细菌中跨细胞质膜转运蛋白质
OAAI
OAAI分享收藏
查看更多内容