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Related Concept Videos

SBAR II: Application of SBAR01:14

SBAR II: Application of SBAR

SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
SBAR Report from a Nurse to a Health Care Provider
S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...
Attribution Theory00:56

Attribution Theory

Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958). An internal factor is an...
Impression Management Techniques IV: Altercasting01:14

Impression Management Techniques IV: Altercasting

Altercasting is a strategic communication technique in which an individual imposes a specific identity or social role onto another person to influence their behavior and shape the interaction. By presuming a role—such as “responsible leader” or “patient person”—altercasting encourages the target to conform to that identity, often aligning their behavior with the expectations associated with the role. The power of this tactic lies in its subtlety; once a role is assigned, it becomes socially...
The Availability Heuristic01:08

The Availability Heuristic

A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

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Related Experiment Video

Updated: Jul 5, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

BEACON: BERTopic-augmented context-aware co-attention recommender system.

Liye Shi1, Pengfei Shao2, Jiayi Chen3,4

  • 1School of Information and Intelligence Engineering, Zhejiang Wanli University, No. 8, Qianhu South Road, Ningbo, 315000, China.

Scientific Reports
|July 3, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces BEACON, a recommender system that improves accuracy by selecting relevant user reviews and using BERTopic to identify meaningful aspects. This approach enhances recommendations by focusing on pertinent information.

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Related Experiment Videos

Last Updated: Jul 5, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

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Published on: December 15, 2023

Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model
06:30

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

Published on: May 24, 2019

Area of Science:

  • Artificial Intelligence
  • Information Retrieval
  • Natural Language Processing

Background:

  • Recommender systems aim to extract user preferences from reviews.
  • Existing methods struggle with selecting relevant auxiliary reviews and accurately modeling fine-grained aspects.
  • Data sparsity and context-agnostic topic models hinder performance.

Purpose of the Study:

  • To propose BEACON, a novel recommender system addressing challenges in auxiliary review selection and aspect modeling.
  • To enhance the accuracy of rating prediction in recommender systems.

Main Methods:

  • Developed a Context-Aware Auxiliary Review Selection Module using target item context for review selection.
  • Introduced a BERTopic-Enhanced Aspect-Guided Representation Module for discovering semantically coherent aspects.
  • Utilized a Factorization Machine Rating Prediction Module for higher-order feature interactions.

Main Results:

  • BEACON effectively selects pertinent user and auxiliary reviews, focusing on relevant textual evidence.
  • The BERTopic integration enables automatic discovery of high-quality, semantically coherent aspects.
  • Comprehensive experiments on Amazon datasets show BEACON surpasses existing baselines in rating prediction accuracy.

Conclusions:

  • BEACON offers a superior approach to recommender systems by effectively handling data sparsity and improving aspect representation.
  • The proposed modules significantly enhance the ability to model user preferences and predict ratings accurately.