mlsanityData to 7 Oct 2026

Deep Learning Based Page Creation for Improving E-Commerce Organic Search Traffic

arXiv 2209.10792 ↗First posted 22 Sep 2022cs.CLcited by 2

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Abstract (arXiv)

Organic search comprises a large portion of the total traffic for e-commerce companies. One approach to expand company's exposure on organic search channel lies on creating landing pages having broader coverage on customer intentions. In this paper, we present a transformer language model based organic channel page management system aiming at increasing prominence of the company's overall clicks on the channel. Our system successfully handles the creation and deployment process of millions of new landing pages. We show and discuss the real-world performances of state-of-the-art language representation learning method, and reveal how we find them as the production-optimal solutions.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 22 Sep 2022.

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8 of 24 references.

References · 8

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  3. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  4. 2015Cumulative Prospect Theory Meets Reinforcement Learning: Prediction and Controlcited by 34
  5. 2020Time-Invariance Coefficients Tests with the Adaptive Multi-Factor Modelcited by 7
  6. 2021A News-based Machine Learning Model for Adaptive Asset Pricingcited by 7
  7. 2020FrequentNet: A Novel Interpretable Deep Learning Model for Image Classificationcited by 5
  8. 2021Clustering Structure of Microstructure Measurescited by 3

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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.