.png){kind=icon}
返回
Fusing temporal and semantic dependencies for session-based recommendation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ipm.2024.103896.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 (SBR) predicts the next item in user sequences. Existing research focuses on item transition patterns, neglecting semantic information dependencies crucial for understanding users' preferences. Incorporating semantic characteristics is vital for accurate recommendations, especially in applications like user purchase sequences. In this paper, to tackle the above issue, we novelly propose a framework that hierarchically fuses temporal and semantic dependencies. Technically, we present the Item Transition Dependency Module and Semantic Dependency Module based on the whole session set: (i) Item Transition Dependency Module is exclusively to learn the item embeddings through temporal relations and utilizes item transitions from both global and local levels; (ii) Semantic Dependency Module develops mutually independent embeddings of both sessions and items via stable interaction relations. In addition, under the unified organization of the Cross View, semantic information is adaptively incorporated into the temporal dependency learning and used to improve the performance of SBR. Extensive experiments on three large-scale real-world datasets show the superiority of our framework over current state-of-the-art methods. In particular, our model improves its performance over SOTA on all three datasets, with 5.5%, 0.2%, and 3.0% improvements on Recall@20, and 5.8%, 4.6%, and 2.0% improvements on MRR@20, respectively.
Keyword:
Session-based recommendation
Contrastive learning
Graph neural network
期刊
I
IF:
6.9
论文数:
5.2K
被引数:
1.4W
机构
引用论文
.png){kind=icon}
Product advertising recommendation in e-commerce based on deep learning and distributed expression
PREAI
分享收藏
Noise-reducing graph neural network with intent-target co-action for session-based recommendation
PREAI
分享收藏
查看更多内容