Multi-view Denoising Graph Auto-Encoders on Heterogeneous Information Networks for Cold-start Recommendation

冷启动(汽车) 计算机科学 推论 编码器 机器学习 任务(项目管理) 情报检索 图形 语义学(计算机科学) 人工智能 推荐系统 数据挖掘 理论计算机科学 工程类 航空航天工程 经济 管理 程序设计语言 操作系统
作者
Jiawei Zheng,Qianli Ma,Hao Gu,Zhenjing Zheng
出处
期刊:Knowledge Discovery and Data Mining 被引量:30
标识
DOI:10.1145/3447548.3467427
摘要

Cold-start recommendation is a challenging problem due to the lack of user-item interactions. Recently, heterogeneous information network~(HIN)-based recommendation methods use rich auxiliary information to enhance users and items' connections, helping alleviate the cold-start problem. Despite progress, most existing methods model HINs under traditional supervised learning settings, ignoring the gaps between training and inference procedures in cold-start scenarios. In this paper, we regard cold-start recommendation as a missing data problem where some user-item interaction data are missing. Inspired by denoising auto-encoders that train a model to reconstruct the input from its corrupted version, we propose a novel model called Multi-view Denoising Graph Auto-Encoders~(MvDGAE) on HINS. Specifically, we first extract multifaceted meaningful semantics on HINs as multi-views for both users and items, effectively enhancing user/item relationships on different aspects. Then we conduct the training procedure by randomly dropping out some user-item interactions in the encoder while forcing the decoder to use these limited views to recover the full views, including the missing ones. In this way, the complementary representations for both users and items are more informative and robust to adjust to cold-start scenarios. Moreover, the decoder's reconstruction goals are multi-view user-user and item-item relationship graphs rather than the original input graphs, which make the features of similar users (or items) in the meta-paths closer together. Finally, we adopt a Bayesian task weight learner to balance multi-view graph reconstruction objectives automatically. Extensive experiments on both public benchmark datasets and a large-scale industry dataset WeChat Channel demonstrate that MvDGAE significantly outperforms the state-of-the-art recommendation models in various cold-start scenarios. The case studies also illustrate that MvDGAE has potentially good interpretability.
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
PDF的下载单位、IP信息已删除 (2025-6-4)

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
感动的念双完成签到,获得积分10
刚刚
酷波er应助chenkaixin采纳,获得10
1秒前
vanilla完成签到,获得积分10
1秒前
小青蛙OA完成签到,获得积分10
2秒前
蓁蓁发布了新的文献求助10
2秒前
Orange应助hao采纳,获得10
2秒前
量子星尘发布了新的文献求助10
2秒前
cyndi完成签到,获得积分0
5秒前
6秒前
贰鸟应助七月流火采纳,获得10
7秒前
yulia完成签到 ,获得积分10
8秒前
佳期如梦完成签到 ,获得积分10
8秒前
9秒前
阿Q完成签到,获得积分10
10秒前
11秒前
彬琪发布了新的文献求助10
12秒前
12秒前
13秒前
13秒前
雨木目完成签到,获得积分10
14秒前
小鱼完成签到,获得积分10
14秒前
chenkaixin发布了新的文献求助10
14秒前
梦XING完成签到 ,获得积分10
15秒前
eternity136发布了新的文献求助10
15秒前
vv完成签到,获得积分10
16秒前
16秒前
哈哈哈哈发布了新的文献求助20
16秒前
KANG完成签到,获得积分10
17秒前
义气黄焖排骨完成签到,获得积分10
17秒前
18秒前
如梦如画发布了新的文献求助10
18秒前
Hannes应助15902933324sjc采纳,获得10
19秒前
19秒前
19秒前
梓默完成签到 ,获得积分10
19秒前
我是老大应助Japan采纳,获得10
20秒前
彬琪完成签到,获得积分10
20秒前
红尘踏歌完成签到,获得积分10
20秒前
不忘初心发布了新的文献求助10
21秒前
18746005898完成签到 ,获得积分10
21秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 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 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038619
求助须知:如何正确求助?哪些是违规求助? 3576294
关于积分的说明 11375058
捐赠科研通 3306084
什么是DOI,文献DOI怎么找? 1819374
邀请新用户注册赠送积分活动 892698
科研通“疑难数据库(出版商)”最低求助积分说明 815066