CSPM: A Contrastive Spatiotemporal Preference Model for CTR Prediction
  in On-Demand Food Delivery Services
Guyu Jiang Xiaoyun Li Rongrong Jing Ruoqi Zhao Xingliang Ni Guodong Cao Ning Hu
Click-through rate (CTR) prediction is a crucial task in the context of an
online on-demand food delivery (OFD) platform for precisely estimating the
probability of a user clicking on food items. Unlike universal e-commerce
platforms such as Taobao and Amazon, user behaviors and interests on the OFD
platform are more location and time-sensitive due to limited delivery ranges
and regional commodity supplies. However, existing CTR prediction algorithms in
OFD scenarios concentrate on capturing interest from historical behavior
sequences, which fails to effectively model the complex spatiotemporal
information within features, leading to poor performance. To address this
challenge, this paper introduces the Contrastive Sres under different search
states using three modules: contrastive spatiotemporal representation learning
(CSRL), spatiotemporal preference extractor (StPE), and spatiotemporal
information filter (StIF). CSRL utilizes a contrastive learning framework to
generate a spatiotemporal activation representation (SAR) for the search
action. StPE employs SAR to activate users' diverse preferences related to
location and time from the historical behavior sequence field, using a
multi-head attention mechanism. StIF incorporates SAR into a gating network to
automatically capture important features with latent spatiotemporal effects.
Extensive experiments conducted on two large-scale industrial datasets
demonstrate the state-of-the-art performance of CSPM. Notably, CSPM has been
successfully deployed in Alibaba's online OFD platform Ele.me, resulting in a
significant 0.88% lift in CTR, which has substantial business implications.