亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整的填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning

计算机科学 人工智能 图形 机器学习 自然语言处理 理论计算机科学
作者
Ding Zou,Wei Wei,Ziyang Wang,Xian-Ling Mao,Feida Zhu,Rui Fang,Dangyang Chen
标识
DOI:10.1145/3511808.3557358
摘要

Incorporating Knowledge Graphs (KG) into recommeder system as side information has attracted considerable attention. Recently, the technical trend of Knowledge-aware Recommendation (KGR) is to develop end-to-end models based on graph neural networks (GNNs). However, the extremely sparse user-item interactions significantly degrade the performance of the GNN-based models, from the following aspects: 1) the sparse interaction, itself, means inadequate supervision signals and limits the supervised GNN-based models; 2) the combination of sparse interactions (CF part) and redundant KG facts (KG part) further results in an unbalanced information utilization. Besides, the GNN paradigm aggregates local neighbors for node representation learning, while ignoring the non-local KG facts and making the knowledge extraction insufficient. Inspired by the recent success of contrastive learning in mining supervised signals from data itself, in this paper, we focus on exploring contrastive learning in KGR and propose a novel multi-level interactive contrastive learning mechanism, to alleviate the aforementioned challenges. Different from traditional contrastive learning methods which contrast nodes of two generated graph views, interactive contrastive mechanism conducts layer-wise self-supervised learning by contrasting layers of different parts within graphs, which is also an "interaction" action. Specifically, we first construct local and non-local graphs for user/item in KG, exploring more KG facts for KGR. Then an intra-graph level interactive contrastive learning is performed within each local/non-local graph, which contrasts layers of the CF and KG parts, for more consistent information leveraging. Besides, an inter-graph level interactive contrastive learning is performed between the local and non-local graphs, for sufficiently and coherently extracting non-local KG signals. Extensive experiments conducted on three benchmark datasets show the superior performance of our proposed method over the state-of-the-arts. The implementations are available at: https://github.com/CCIIPLab/KGIC.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
科研通AI2S应助科研通管家采纳,获得10
2秒前
快乐的如风完成签到,获得积分10
4秒前
6秒前
英勇羿发布了新的文献求助100
8秒前
40秒前
46秒前
47秒前
诺hn完成签到 ,获得积分10
52秒前
田様应助LL采纳,获得10
1分钟前
1分钟前
LL发布了新的文献求助10
1分钟前
1分钟前
么么么发布了新的文献求助10
1分钟前
1分钟前
么么么完成签到 ,获得积分10
1分钟前
1分钟前
李九妹完成签到 ,获得积分10
1分钟前
经冰夏完成签到 ,获得积分10
1分钟前
1分钟前
科研通AI2S应助科研通管家采纳,获得10
2分钟前
科研通AI2S应助科研通管家采纳,获得10
2分钟前
大个应助含蓄戾采纳,获得10
2分钟前
轩仔发布了新的文献求助10
2分钟前
2分钟前
NCL完成签到,获得积分10
2分钟前
2分钟前
好巧完成签到,获得积分10
2分钟前
含蓄戾完成签到,获得积分10
2分钟前
2分钟前
含蓄戾发布了新的文献求助10
2分钟前
医者仁心完成签到,获得积分10
2分钟前
Julie发布了新的文献求助10
2分钟前
打打应助NCL采纳,获得10
2分钟前
搜集达人应助材料摆渡人采纳,获得10
2分钟前
2分钟前
choyng发布了新的文献求助10
2分钟前
科研通AI2S应助XIN采纳,获得10
3分钟前
热带蚂蚁完成签到 ,获得积分10
3分钟前
3分钟前
3分钟前
高分求助中
Evolution 10000
Sustainability in Tides Chemistry 2800
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
юрские динозавры восточного забайкалья 800
English Wealden Fossils 700
Foreign Policy of the French Second Empire: A Bibliography 500
Chen Hansheng: China’s Last Romantic Revolutionary 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3146703
求助须知:如何正确求助?哪些是违规求助? 2798015
关于积分的说明 7826470
捐赠科研通 2454516
什么是DOI,文献DOI怎么找? 1306328
科研通“疑难数据库(出版商)”最低求助积分说明 627704
版权声明 601522