RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph

Ruijie Wang, Zheng Li, Danqing Zhang, Qingyu Yin, Tong Zhao, Bing Yin, Tarek Abdelzaher

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the increasing demands on e-commerce platforms, numerous user action history is emerging. Those enriched action records are vital to understand users' interests and intents. Recently, prior works for user behavior prediction mainly focus on the interactions with product-side information. However, the interactions with search queries, which usually act as a bridge between users and products, are still under investigated. In this paper, we explore a new problem named temporal event forecasting, a generalized user behavior prediction task in a unified query product evolutionary graph, to embrace both query and product recommendation in a temporal manner. To fulfill this setting, there involves two challenges: (1) the action data for most users is scarce; (2) user preferences are dynamically evolving and shifting over time. To tackle those issues, we propose a novel Retrieval-Enhanced Temporal Event (RETE) forecasting framework. Unlike existing methods that enhance user representations via roughly absorbing information from connected entities in the whole graph, RETE efficiently and dynamically retrieves relevant entities centrally on each user as high-quality subgraphs, preventing the noise propagation from the densely evolutionary graph structures that incorporate abundant search queries. And meanwhile, RETE autoregressively accumulates retrieval-enhanced user representations from each time step, to capture evolutionary patterns for joint query and product prediction. Empirically, extensive experiments on both the public benchmark and four real-world industrial datasets demonstrate the effectiveness of the proposed RETE method.

Original languageEnglish (US)
Title of host publicationWWW 2022 - Proceedings of the ACM Web Conference 2022
PublisherAssociation for Computing Machinery, Inc
Pages462-472
Number of pages11
ISBN (Electronic)9781450390965
DOIs
StatePublished - Apr 25 2022
Event31st ACM World Wide Web Conference, WWW 2022 - Virtual, Online, France
Duration: Apr 25 2022Apr 29 2022

Publication series

NameWWW 2022 - Proceedings of the ACM Web Conference 2022

Conference

Conference31st ACM World Wide Web Conference, WWW 2022
Country/TerritoryFrance
CityVirtual, Online
Period4/25/224/29/22

Keywords

  • Dynamic Graph Learning
  • E-commerce
  • Temporal Event Forecasting

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

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