Jove
Visualize
Contact Us
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 Concept Videos

Language and Cognition01:27

Language and Cognition

442
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
442
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

116
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
116
Language Development01:22

Language Development

450
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
450
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

258
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
258
Associative Learning01:27

Associative Learning

576
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...
576
Language01:16

Language

425
Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
425

You might also read

Related Articles

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

Sort by
Same author

Establishing a Multimodal Model for Predicting Lymphovascular Invasion in Breast Cancer Using Deep Transfer Learning Based on Ultrasound and Dynamic Contrast-Enhanced Magnetic Resonance Imaging.

Oncology research and treatment·2026
Same author

Association between glucolipid metabolic indicators and the risk of suspected precocious puberty in children living with obesity: a retrospective cohort study.

Frontiers in pediatrics·2026
Same author

Injectable pH-responsive Bletilla striata polysaccharide-sodium alginate hydrogel promoting peripheral nerve regeneration.

International journal of biological macromolecules·2026
Same author

Pemt Inhibition-Mediated Vdac1 Oligomerization Regulates Mitochondrial Dysfunction, Apoptosis, and Inflammation in High-Fat Diet-Derived Liver Injury.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

hERG channel blockade and additive interactions of magnolol and honokiol from Magnolia species.

Toxicology letters·2026
Same author

Linker Engineering toward NIR-II Metal-Organic Framework with Maximal Emission beyond 1000 nm for Inflammatory Bowel Disease Imaging.

Journal of the American Chemical Society·2026

Related Experiment Video

Updated: Sep 12, 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

682

Leveraging multimodal large language model for multimodal sequential recommendation.

Zhaoliang Wang1,2, Baisong Liu3, Weiming Huang1

  • 1Faculty of Information Science and Engineering, Ningbo University, Ningbo, 315211, People's Republic of China.

Scientific Reports
|August 7, 2025
PubMed
Summary

Multimodal large language models (MLLMs) enhance sequential recommendation systems by fusing multimodal features and modeling dynamic user preferences. MLLM-SRec improves recommendation precision and robustness by leveraging MLLMs for better cross-modal understanding.

Keywords:
Multimodal large language modelMultimodal recommendationRecommender systemsSequential recommendation

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

579
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.1K

Related Experiment Videos

Last Updated: Sep 12, 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

682
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

579
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.1K

Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Machine Learning

Background:

  • Conventional multimodal recommendation systems struggle with insufficient information exploitation, limited multimodal feature recognition, and ineffective dynamic preference modeling.
  • Existing approaches often rely on unimodal data, failing to capture cross-modal preferences and the evolution of user interests in sequential interactions.
  • Multimodal large language models (MLLMs) offer advanced cross-modal comprehension and world knowledge, presenting a promising avenue for recommendation system enhancement.

Purpose of the Study:

  • To introduce MLLM-SRec, a novel sequential recommendation architecture leveraging MLLMs to address limitations in current multimodal recommendation systems.
  • To develop a multimodal feature fusion mechanism for unified item representations, aligning vision and text while mitigating cross-modal differences and noise.
  • To design a temporal-aware module for dynamic user preference modeling and integrate it with Chain-of-Thought prompting for effective knowledge transfer.

Main Methods:

  • Developed a multimodal feature fusion mechanism using MLLMs to create unified semantic item representations, ensuring semantic alignment between visual and textual data.
  • Implemented a temporal-aware user behavior comprehension module to capture the dynamic evolution of user preferences within sequential interaction data.
  • Employed supervised fine-tuning combined with multistep Chain-of-Thought prompting to optimize knowledge transfer from pre-trained MLLMs to the recommendation task.

Main Results:

  • The proposed MLLM-SRec architecture achieved significant improvements over state-of-the-art baselines across four benchmark datasets.
  • The method substantially enhanced the precision of recommendation results.
  • MLLM-SRec demonstrated superior robustness and adaptability in multimodal sequential recommendation scenarios.

Conclusions:

  • MLLM-SRec effectively addresses the challenges of multimodal feature recognition and dynamic preference modeling in sequential recommendation.
  • The architecture validates the significant potential of MLLMs for advancing sequential recommendation tasks by improving multimodal interaction data utilization.
  • The findings offer new methodological insights for multimodal sequence recommendation research and highlight the benefits of MLLM integration.