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When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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As formulated by John Thibaut and Harold Kelley, Social Exchange Theory explains human relationships as economic-like exchanges that maximize rewards and minimize costs. This theory suggests that individuals engage in relationships to gain benefits and reduce burdens, similar to economic transactions. It has been widely applied to various types of relationships, including romantic, professional, and social interactions.Rewards and Costs in RelationshipsRelationship rewards include emotional...
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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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Usability Evaluation of Augmented Reality: A Neuro-Information-Systems Study
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E-commerce recommender system design based on web information extraction and sentiment analysis.

Jinfeng Feng1

  • 1Jiaozuo Normal College, Jiaozuo, China.

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This study introduces an advanced e-commerce recommendation system using web data extraction and sentiment analysis. The system achieves superior accuracy in product recommendations and user emotion insights compared to existing platforms.

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Area of Science:

  • E-commerce
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • E-commerce platforms require accurate product information and user sentiment analysis for effective recommendations.
  • Existing recommendation systems face challenges in cross-platform data extraction and nuanced sentiment understanding.

Purpose of the Study:

  • To develop an improved e-commerce recommendation system leveraging enhanced web information extraction and sentiment analysis.
  • To increase the precision of cross-platform commodity information extraction and sentiment scoring.

Main Methods:

  • Implemented an improved S-PageRank algorithm and dynamic topic library for precise web page information extraction.
  • Utilized a comprehensive attention mechanism model, combining topic modeling (LDA) and bidirectional long short-term memory networks (BiLSTM), for sentiment analysis.
  • Employed template-based web page information extraction, outperforming the Document Object Model (DOM) method.

Main Results:

  • Achieved a 90% precision rate in cross-platform commodity information extraction, surpassing traditional S-PageRank.
  • The template-based extraction method showed a 10% higher precision rate than DOM-based methods.
  • The attention-based sentiment analysis model accurately calculated sentiment scores per topic, with peak prediction accuracy at 7 LDA topics, outperforming prior methods in accuracy, recall, and F-score.

Conclusions:

  • The developed recommendation system excels in predicting sentiment trends and analyzing emotional drivers in customer feedback.
  • Demonstrated superior prediction and analysis accuracy compared to established systems like Amazon and Netflix.
  • Offers enhanced personalized product recommendations for users and deeper emotional insights for merchants.