  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.
