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.
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