Cross-channel Recommendation for Multi-channel Retail
arxiv(2024)
摘要
An increasing number of brick-and-mortar retailers are expanding their
channels to the online domain, transforming them into multi-channel retailers.
This transition emphasizes the need for cross-channel recommender systems,
aiming to enhance revenue across both offline and online channels. Given that
each retail channel represents a separate domain with a unique context, this
can be regarded as a cross-domain recommendation (CDR). However, the existing
studies on CDR did not address the scenarios where both users and items
partially overlap across multi-retail channels which we define as
"cross-channel retail recommendation (CCRR)". This paper introduces our
original work on CCRR using real-world datasets from a multi-channel retail
store. Specifically, (1) we present significant challenges in integrating user
preferences across both channels. (2) Accordingly, we propose a novel model for
CCRR using a channel-wise attention mechanism to capture different user
preferences for the same item on each channel. We empirically validate our
model's superiority in addressing CCRR over existing models. (3) Finally, we
offer implications for future research on CCRR, delving into our experiment
results.
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