Towards An Efficient LLM Training Paradigm for CTR Prediction

• URL: http://arxiv.org/abs/2503.01001v3
• Date: Sat, 15 Mar 2025 14:45:21 GMT
• Title: Towards An Efficient LLM Training Paradigm for CTR Prediction
• Authors: Allen Lin, Renqin Cai, Yun He, Hanchao Yu, Jing Qian, Rui Li, Qifan Wang, James Caverlee,
• Abstract summary: Large Language Models (LLMs) can significantly outperform conventional click-through-rate (CTR) prediction approaches.<n>To train LLMs for CTR prediction, most existing studies adopt the prevalent ''sliding-window'' paradigm.<n>We propose a novel training paradigm, namely Dynamic Target Isolation (DTI), that structurally parallelizes the training of $k$ target interactions.
• Score: 37.20013051226115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have demonstrated tremendous potential as the next-generation ranking-based recommendation system. Many recent works have shown that LLMs can significantly outperform conventional click-through-rate (CTR) prediction approaches. Despite such promising results, the computational inefficiency inherent in the current training paradigm makes it particularly challenging to train LLMs for ranking-based recommendation tasks on large datasets. To train LLMs for CTR prediction, most existing studies adopt the prevalent ''sliding-window'' paradigm. Given a sequence of $m$ user interactions, a unique training prompt is constructed for each interaction by designating it as the prediction target along with its preceding $n$ interactions serving as context. In turn, the sliding-window paradigm results in an overall complexity of $O(mn^2)$ that scales linearly with the length of user interactions. Consequently, a direct adoption to train LLMs with such strategy can result in prohibitively high training costs as the length of interactions grows. To alleviate the computational inefficiency, we propose a novel training paradigm, namely Dynamic Target Isolation (DTI), that structurally parallelizes the training of $k$ (where $k >> 1$) target interactions. Furthermore, we identify two major bottlenecks - hidden-state leakage and positional bias overfitting - that limit DTI to only scale up to a small value of $k$ (e.g., 5) then propose a computationally light solution to effectively tackle each. Through extensive experiments on three widely adopted public CTR datasets, we empirically show that DTI reduces training time by an average of $\textbf{92%}$ (e.g., from $70.5$ hrs to $5.31$ hrs), without compromising CTR prediction performance.

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