Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

Predicting Products: Substitution vs. Elimination

Predicting Products: Substitution vs. Elimination

When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:

Predicting Products: SN1 vs. SN2

Predicting Products: S_N1 vs. S_N2

Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
With increased substitution on the alkyl halide,...

Hindsight Biases

Hindsight Biases

Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?

Maxam-Gilbert Sequencing

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...

The Anchoring-and-Adjustment Heuristic

The Anchoring-and-Adjustment Heuristic

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...

You might also read

Related Articles

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

Sort by

Same author

Liposome-cyclodextrin synergistic system for enhanced chiral separation of Fmoc-amino acid enantiomers in capillary electrophoresis.

Mikrochimica acta·2026

Same author

Stroke-Related Animal Models: Methodologies, Applications, and Translational Perspectives.

Journal of visualized experiments : JoVE·2026

Same author

An Ultra-Thin and Wideband Low-Frequency Absorber Based on Periodic Resistance Film.

Materials (Basel, Switzerland)·2026

Same author

A highly stable C18-modified polystyrene-divinylbenzene stationary phase on porous silica for HPLC.

Journal of chromatography. A·2026

Same author

Mamba-based brain tumor segmentation of incomplete multi-modal MR images.

Quantitative imaging in medicine and surgery·2026

Same author

Synergistic Zn/Al Co-Doping and Sodium Enrichment Enable Reversible Phase Transitions in High-Performance Layered Sodium Cathodes.

Molecules (Basel, Switzerland)·2025

Search research articles

Related Experiment Video

Updated: May 23, 2025

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

Published on: May 3, 2018

Knowledge-Guided Semantically Consistent Contrastive Learning for sequential recommendation.

Chenglong Shi¹, Surong Yan¹, Shuai Zhang¹

¹Zhejiang University of Finance and Economics, Hangzhou, 310018, China.

Neural Networks : the Official Journal of the International Neural Network Society

|March 8, 2025

Summary

This summary is machine-generated.

Knowledge-guided contrastive learning enhances sequential recommendation by using item knowledge graphs to create semantically consistent data views. This approach overcomes limitations of random augmentations, improving recommendation performance.

Keywords:

Contrastive learning Knowledge graph Semantic consistency Sequential recommendation

More Related Videos

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

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

Published on: December 6, 2024

A Two-interval Forced-choice Task for Multisensory Comparisons

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

Related Experiment Videos

Last Updated: May 23, 2025

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

Published on: May 3, 2018

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

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

Published on: December 6, 2024

A Two-interval Forced-choice Task for Multisensory Comparisons

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

Area of Science:

Artificial Intelligence
Machine Learning
Data Science

Background:

Contrastive learning is dominant in sequential recommendation for addressing data sparsity.
Existing methods suffer from performance degradation due to inconsistent semantics from random augmentations.
Information scarcity limits the effectiveness of current augmentation strategies.

Purpose of the Study:

To propose a novel Knowledge-Guided Semantically consistent Contrastive Learning (KGSCL) model for sequential recommendation.
To leverage Item Knowledge Graphs (IKG) for creating semantically consistent data augmentations.
To improve recommendation performance, robustness, and model convergence.

Main Methods:

Introduced knowledge-guided augmentation operations (KG-substitute, KG-insert) using IKG neighbors.
Developed a co-occurrence-based sampling strategy for selecting correlated neighbors.
Implemented a view-target contrastive learning (CL) approach to model correlations between views and target items.

Main Results:

KGSCL demonstrated superior recommendation performance across six datasets.
The model showed enhanced robustness compared to 14 state-of-the-art competitors.
KGSCL achieved better model convergence.

Conclusions:

Knowledge-guided semantic consistency in contrastive learning significantly improves sequential recommendation.
Leveraging Item Knowledge Graphs offers a promising direction for addressing augmentation challenges.
KGSCL provides an effective and robust solution for sequential recommendation systems.