pith. sign in

arxiv: 2104.10584 · v1 · pith:SZA5OOINnew · submitted 2021-04-21 · 💻 cs.IR · cs.LG

Deep Learning for Click-Through Rate Estimation

Weinan Zhang , Jiarui Qin , Wei Guo , Ruiming Tang , Xiuqiang He This is my paper
classification 💻 cs.IR cs.LG
keywords deepmodelsestimationlearningclick-throughonlineplatformsrate
0
0 comments X
read the original abstract

Click-through rate (CTR) estimation plays as a core function module in various personalized online services, including online advertising, recommender systems, and web search etc. From 2015, the success of deep learning started to benefit CTR estimation performance and now deep CTR models have been widely applied in many industrial platforms. In this survey, we provide a comprehensive review of deep learning models for CTR estimation tasks. First, we take a review of the transfer from shallow to deep CTR models and explain why going deep is a necessary trend of development. Second, we concentrate on explicit feature interaction learning modules of deep CTR models. Then, as an important perspective on large platforms with abundant user histories, deep behavior models are discussed. Moreover, the recently emerged automated methods for deep CTR architecture design are presented. Finally, we summarize the survey and discuss the future prospects of this field.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Understanding DNNs in Feature Interaction Models: A Dimensional Collapse Perspective

    cs.LG 2026-04 unverdicted novelty 6.0

    DNNs mitigate dimensional collapse of embeddings in feature interaction models, shown via parallel and stacked experiments plus gradient analysis.

  2. Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation

    cs.IR 2026-03 unverdicted novelty 6.0

    MoS applies theme-aware routing to extract multi-scale theme-specific subsequences from noisy long user sequences, achieving state-of-the-art recommendation performance with fewer FLOPs than comparable MoE models.

  3. Beyond Dense Connectivity: Explicit Sparsity for Scalable Recommendation

    cs.IR 2026-04 unverdicted novelty 5.0

    SSR uses static random filters and iterative competitive sparse mechanisms to explicitly enforce sparsity in recommendation models, outperforming dense baselines on public and billion-scale industrial datasets.