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Related Experiment Video

Updated: Apr 9, 2026

Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model
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Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model

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E-Commerce Network Search System Based on Target Webpage Positioning and Sentiment Analysis Recommendation.

Yu-Chung Hsiao1

  • 1Business School, Nanfang College Guangzhou, Guangzhou, China.

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|April 8, 2026
PubMed
Summary
This summary is machine-generated.

A new sentiment analysis recommendation model enhances e-commerce product search efficiency and user satisfaction. This model improves accuracy and reduces search time, offering a superior online shopping experience.

Keywords:
attention mechanismbidirectional long short-term memory networkdocument object modele-commerce search systemimproved page ranking algorithmsentiment analysis recommendation

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Area of Science:

  • Computer Science
  • Information Retrieval
  • Artificial Intelligence

Background:

  • E-commerce product search efficiency and user satisfaction are critical.
  • Existing sentiment analysis in recommendations lacks accuracy.
  • Need for improved product search and recommendation systems.

Purpose of the Study:

  • To propose a novel webpage localization and sentiment analysis recommendation model.
  • To develop an e-commerce network search system based on this model.
  • To enhance product search accuracy and user satisfaction in e-commerce.

Main Methods:

  • Combined an improved web search algorithm with a bidirectional long short-term memory (BiLSTM) network and an attention mechanism.
  • Developed a sentiment analysis recommendation model.
  • Integrated the model into an e-commerce network search system.

Main Results:

  • Sentiment analysis recommendation model achieved 98.88% accuracy, 98.92% recall, and 97.78% F1 score.
  • Recognition accuracy for four emotional tendencies exceeded 95%.
  • Integrated system showed 87.6 ms average search time, 1.42% missed-search rate, and 99.34% user satisfaction.

Conclusions:

  • The proposed model significantly outperforms existing methods in sentiment analysis and product recommendation.
  • The integrated system offers a practical, high-performance solution for sentiment-aware e-commerce search.
  • This research provides a foundation for future advancements in e-commerce search systems.