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STARDOM: Semantic Aware Deep Hierarchical Forecasting Model for Search Traffic Prediction by Yucheng Lu; Qiang Ji; Liang Wang; Tianshu Wu; Hongbo Deng; Jian Xu; Bo Zheng is a scholarly article available to read on EtoBox.

What is STARDOM: Semantic Aware Deep Hierarchical Forecasting Model for Search Traffic Prediction about?

We study the search traffic forecasting problem for guaranteed search advertising (GSA) application in e-commerce platforms. The consumers express their purchase intents by posing queries to the ecommerce search engine. GSA is a type of guaranteed delivery (GD) advertising strategy, which forecasts the traffic of search queries, and charges the advertisers according to the predicted volumes of search queries the advertisers willing to buy. We employ the time series forecasting method to make the search traffic prediction. Different from existing time series prediction methods, search queries are semantically meaningful, with semantically similar queries possessing similar time series. And they can be grouped according to the brands or categories they belong to, exhibiting hierarchical structures. To fully take advantage of these characteristics, we design a SemanTic AwaRe Deep hierarchical fOrecasting Model (STARDOM for short) which explores the queries' semantic information and the hierarchical structures formed by the queries. Specifically, to exploit hierarchical structure, we propose a reconciliation learning module. It leverages deep learning model to learn the reconciliation

Author

Yucheng Lu; Qiang Ji; Liang Wang; Tianshu Wu; Hongbo Deng; Jian Xu; Bo Zheng

Publisher

ACM

Published

2022

Language

EN