arXiv:2508.03668v2 Announce Type: replace Abstract: Click-Through Rate (CTR) prediction, a core task in recommendation systems, estimates user click likelihood using historical behavioral data. Modeling user behavior sequences as text to leverage Language Models (LMs) for this task has gained traction, owing to LMs' strong semantic understanding and contextual modeling capabilities. However, a critical structural gap exists: user behavior sequences consist of discrete actions connected by semantically empty separators, differing fundamentally from the coherent natural language in LM pre-traini
The continuous evolution of AI and language models necessitates constant refinement for specialized applications like recommendation systems, bridging the gap between general AI capabilities and specific industry needs.
Improving Click-Through Rate (CTR) prediction directly enhances the efficiency and profitability of digital advertising and e-commerce platforms, impacting major tech companies and their revenue streams.
This research introduces a novel method (CTR-Sink) to adapt large language models (LMs) more effectively for structured behavioral data, moving beyond their typical natural language domain to improve recommendation accuracy.
- · E-commerce platforms
- · Ad-tech companies
- · AI researchers specializing in recommendation systems
- · Consumers (through more relevant recommendations)
- · Traditional recommendation algorithms
- · Less adaptable AI models
Increased efficiency and revenue for online platforms through more accurate user behavior prediction.
Accelerated adoption of advanced AI techniques in other data-intensive, non-natural language domains beyond just recommendations.
Enhanced personalization at scale could lead to more entrenched user engagement and competitive advantages for companies that implement these advanced models early.
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