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Digital advertising is a critical part of many e-commerce platforms such as Taobao and Amazon. While in recent years a lot of attention has been drawn to the consumer side including canonical problems like ctr/cvr prediction, the advertiser side, which directly serves advertisers by providing them with marketing tools, is now playing a more and more important role. When speaking of sponsored search, bid keyword recommendation is the fundamental service. This paper addresses the problem of keyword matching, the primary step of keyword recommendation. Existing methods for keyword matching merely consider modeling relevance based on a single type of relation among ads and keywords, such as query clicks or text similarity, which neglects rich heterogeneous interactions hidden behind them. To fill this gap, the keyword matching problem faces several challenges including: 1) how to learn enriched and robust embeddings from complex interactions among various types of objects; 2) how to conduct high-quality matching for new ads that usually lack sufficient data. To address these challenges, we develop a heterogeneous-graph-neural-network-based model for keyword matching named HetMatch, which has been deployed both online and offline at the core sponsored search platform of Alibaba Group. To extract enriched and robust embeddings among rich relations, we design a hierarchical structure to fuse and enhance the relevant neighborhood patterns both on the micro and the macro level. Moreover, by proposing a multi-view framework, the model is able to involve more positive samples for cold-start ads. Experimental results on a large-scale industrial dataset as well as online AB tests exhibit the effectiveness of HetMatch., Comment: 10 pages

Different time series is measured in almost all fields including biology, economics and sociology. A common challenge for using such data is the imputation of the missing values with reasonable ones. Most of existing approaches to data imputation assume that individual's observations are independent to each other, which is rarely the case in real-world. In this paper, we study the social-aware time series imputation problem. Given a social network that represents social relations between individuals, we propose a sequential encoder-decoder-based framework and build a connection between the missing observations and the social context. In particular, the proposed model employs the attention mechanism to incorporate social context and temporal context into the imputation task. Experimental results based on two real-world datasets demonstrate that our approach outperforms 11 different baseline methods.