Jove
Visualize
Contact Us

Related Concept Videos

Self-Schemas02:16

Self-Schemas

32.3K
In general, a schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
32.3K
The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

7.4K
In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
7.4K
Cross-reactivity00:42

Cross-reactivity

31.5K
Overview
31.5K
The Availability Heuristic01:08

The Availability Heuristic

6.4K
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):
6.4K
Tip-of-the-Tongue Phenomenon01:10

Tip-of-the-Tongue Phenomenon

226
The tip-of-the-tongue (TOT) phenomenon is a cognitive experience characterized by a temporary inability to retrieve specific information from memory despite having a strong feeling of knowing the information. Although individuals cannot access the target word or detail, they frequently recall related elements, such as its initial letter, syllable count, or context. This partial retrieval often causes frustration, as one might recognize a familiar face or know that a name starts with a specific...
226
Self-Discrepancy Theory02:45

Self-Discrepancy Theory

18.4K
One influential perspective on what motivates people's behavior is detailed in Tory Higgin's self-discrepancy theory (Higgins, 1987). He proposed that people hold disagreeing internal representations of themselves that lead to different emotional states.  
18.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Research Advances in the Pathogenesis of Sepsis-Associated Encephalopathy.

International journal of molecular sciences·2026
Same author

CoShMDM: Contact and Shape-Aware Latent Motion Diffusion Model for Human Interaction Generation.

IEEE transactions on visualization and computer graphics·2026
Same author

Cross-view contrastive representation learning on meta-path induced graphs with node features for bundle recommendation.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Development and validation of an interpretable machine learning model for early prediction in patients with diabetes and sepsis.

Scientific reports·2025
Same author

Universal and highly sensitive detection of influenza A virus and streptococcus pneumoniae using WGA-modified magnetic SERS nanotags-based lateral flow assay.

Nanomedicine : nanotechnology, biology, and medicine·2025
Same author

How online public opinion evolves before and after policy adjustments in response to major public health emergencies.

Frontiers in public health·2025
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Sep 11, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

681

Domain-aware self-prompting for cross-domain sequential recommendations with natural language explanations.

Tesfaye Fenta Boka1, Zhendong Niu2, Tekie Tsegay Tewolde2

  • 1School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China; Department of Computer Science,Bule Hora University, Bule Hora, Ethiopia.

Neural Networks : the Official Journal of the International Neural Network Society
|August 17, 2025
PubMed
Summary

Domain-Aware Self-Prompting (DASP) enhances cross-domain recommendation by generating natural language explanations. This efficient framework improves recommendation accuracy and provides interpretable insights into user preferences across domains.

Keywords:
Contrastive learningCross-domain sequential recommendationMeta-learningNatural language explanations

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

578
Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.1K

Related Experiment Videos

Last Updated: Sep 11, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

681
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

578
Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.1K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Cross-domain sequential recommendation presents challenges like domain shift and data sparsity.
  • Existing methods often lack efficiency in cross-domain adaptation and coherent explanation generation.

Purpose of the Study:

  • To propose a novel framework, Domain-Aware Self-Prompting (DASP), integrating cross-domain recommendation with natural language explanation generation.
  • To address limitations in efficiency, adaptability, and explainability in multi-domain recommendation systems.

Main Methods:

  • DASP utilizes a domain-invariant self-prompt generator with contrastive alignment for shared user preferences.
  • Lightweight domain adapters with meta-learned initialization enable parameter-efficient adaptation.
  • A cross-domain explanation generator leverages large language models for semantically aligned multi-domain prompts.

Main Results:

  • DASP achieved significant improvements in HR@10 (10.7%) and NDCG@10 (10.5%) on the Movie-Book dataset.
  • Training time was reduced by 54% compared to full large language model fine-tuning.
  • Both qualitative and quantitative analyses confirmed DASP's ability to generate interpretable cross-domain explanations.

Conclusions:

  • DASP offers a scalable and trustworthy solution for cross-domain sequential recommendation.
  • The framework effectively bridges user preferences across domains with coherent explanations.
  • DASP advances the state-of-the-art in efficient, adaptable, and explainable recommendation systems.