A Unified Framework for Multi-Domain CTR Prediction via Large Language Models
CoRR(2023)
摘要
Click-Through Rate (CTR) prediction is a crucial task in online
recommendation platforms as it involves estimating the probability of user
engagement with advertisements or items by clicking on them. Given the
availability of various services like online shopping, ride-sharing, food
delivery, and professional services on commercial platforms, recommendation
systems in these platforms are required to make CTR predictions across multiple
domains rather than just a single domain. However, multi-domain click-through
rate (MDCTR) prediction remains a challenging task in online recommendation due
to the complex mutual influence between domains. Traditional MDCTR models
typically encode domains as discrete identifiers, ignoring rich semantic
information underlying. Consequently, they can hardly generalize to new
domains. Besides, existing models can be easily dominated by some specific
domains, which results in significant performance drops in the other domains
(i.e. the "seesaw phenomenon"). In this paper, we propose a novel solution
Uni-CTR to address the above challenges. Uni-CTR leverages a backbone Large
Language Model (LLM) to learn layer-wise semantic representations that capture
commonalities between domains. Uni-CTR also uses several domain-specific
networks to capture the characteristics of each domain. Note that we design a
masked loss strategy so that these domain-specific networks are decoupled from
backbone LLM. This allows domain-specific networks to remain unchanged when
incorporating new or removing domains, thereby enhancing the flexibility and
scalability of the system significantly. Experimental results on three public
datasets show that Uni-CTR outperforms the state-of-the-art (SOTA) MDCTR models
significantly. Furthermore, Uni-CTR demonstrates remarkable effectiveness in
zero-shot prediction. We have applied Uni-CTR in industrial scenarios,
confirming its efficiency.
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