Click-Through Rate (CTR
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CTR-Sink: Attention Sink for Language Models in Click-Through Rate Prediction
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...
CTR-Sink: Attention Sink for Language Models in Click-Through Rate Prediction
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...
Jointly Optimizing Debiased CTR and Uplift for Coupons Marketing: A Unified Causal Framework
arXiv:2602.12972v2 Announce Type: replace Abstract: In online advertising, marketing interventions such as coupons introduce significant confounding bias into Click-Through Rate (CTR) prediction. Observed clicks reflect a mixture of users' intrinsic preferences and the uplift induced by these interventions. This causes conventional models to miscalibrate base CTRs, which distorts downstream ranking and billing decisions.
Dual-Stream MLP is All You Need for CTR Prediction
arXiv:2606.04944v1 Announce Type: new Abstract: Click-through rate (CTR) prediction holds a pivotal role in online advertising and recommendation systems, where even small improvements can significantly boost revenue. Existing research primarily focuses on designing dual-stream architectures to capture effective complex feature interactions from both explicit and implicit perspectives. However, these approaches are faced with two major challenges: 1) the high complexity of feature...
From Scaling to Structured Expressivity: Rethinking Transformers for CTR Prediction
arXiv:2511.12081v2 Announce Type: replace Abstract: Despite massive investments in scale, deep models for click-through rate (CTR) prediction often exhibit rapidly diminishing returns -- a stark contrast to the {predictable scaling laws} seen in large language models (LLMs). We identify the root cause as a {fundamental} \textit{structural misalignment}: {standard} Transformers assume sequential compositionality, whereas CTR data demand combinatorial reasoning over {heterogeneous} fields. To...
The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit
arXiv:2501.02173v2 Announce Type: replace Abstract: The deployment of Large Language Models (LLMs) in recommender systems for predicting Click-Through Rates (CTR) necessitates a delicate balance between computational efficiency and predictive accuracy. This paper presents an optimization framework that combines Retrieval-Augmented Generation (RAG) with an innovative multi-head early exit architecture to concurrently enhance both aspects. By integrating Graph Convolutional Networks (GCNs) as...
Quantizing Intent: Cross-Domain Semantic IDs from Organic Activity for Industrial Ranking
arXiv:2606.01396v1 Announce Type: new Abstract: Ads click-through rate (CTR) prediction is constrained by sparse user supervision: most users engage with ads infrequently while generating dense behavioral evidence in organic surfaces such as feed. Transferring these cross-domain signals into ads ranking is difficult due to domain mismatch, serving cost, and production complexity. We introduce cross-domain user Semantic IDs (SIDs) derived from organic feed activity and show that behavioral...
ToolRec: Calibrated Preference Alignment for Query Recommendation in On-Device Assistants
arXiv:2606.08466v1 Announce Type: new Abstract: Large Language Models (LLMs) have significantly advanced generative query recommendation. However, existing alignment methods primarily focus on standard chatbot scenarios, falling short in on-device intelligent assistants where users predominantly expect the rapid invocation of system-level tools. Moreover, directly aligning LLMs with real-world click logs introduces severe noise due to varying user activity levels and the failure to emphasize...