  Traditional click-through rate (CTR) prediction models convert the tabular
data into one-hot vectors and leverage the collaborative relations among
features for inferring the user's preference over items. This modeling paradigm
discards essential semantic information. Though some works like P5 and CTR-BERT
have explored the potential of using Pre-trained Language Models (PLMs) to
extract semantic signals for CTR prediction, they are computationally expensive
and suffer from low efficiency. Besides, the beneficial collaborative relations
are not considered, hindering the recommendation performance. To solve these
problems, in this paper, we propose a novel framework \textbf{CTRL}, which is
industrial-friendly and model-agnostic with superior inference efficiency.
Specifically, the original tabular data is first converted into textual data.
Both tabular data and converted textual data are regarded as two different
modalities and are separately fed into the collaborative CTR model and
pre-trained language model. A cross-modal knowledge alignment procedure is
performed to fine-grained align and integrate the collaborative and semantic
signals, and the lightweight collaborative model can be deployed online for
efficient serving after fine-tuned with supervised signals. Experimental
results on three public datasets show that CTRL outperforms the
state-of-the-art (SOTA) CTR models significantly. Moreover, we further verify
its effectiveness on a large-scale industrial recommender system.
