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 Experiment Video

Updated: Oct 9, 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

723

Word Sequential Using Deep LSTM and Matrix Factorization to Handle Rating Sparse Data for E-Commerce Recommender

Hanafi1, Burhanuddin Mohd Aboobaider2

  • 1Faculty of Computer Science, University of Amikom Yogyakarta, Yogyakarta 55283, Indonesia.

Computational Intelligence and Neuroscience
|December 17, 2021
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Associative Learning01:27

Associative Learning

650
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...
650
Stereotype Content Model02:16

Stereotype Content Model

14.9K
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...
14.9K
Multiple Regression01:25

Multiple Regression

3.3K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.3K
Response Surface Methodology01:16

Response Surface Methodology

315
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
315
Aggregates Classification01:29

Aggregates Classification

411
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
411
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

146
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...
146

You might also read

Related Articles

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

Sort by
Same author

Optimizing Feature Selection and Machine Learning Algorithms for Early Detection of Prediabetes Risk: Comparative Study.

JMIR bioinformatics and biotechnology·2025
Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Computational intelligence and neuroscience·2025
Same journal

RETRACTION: Distributed Scheduling Strategy of Virtual Power Plant Using the Particle Swarm Optimization Neural Network under Blockchain Background.

Computational intelligence and neuroscience·2025
See all related articles

This study enhances e-commerce recommender systems by integrating matrix factorization with LSTM and word embedding to interpret product reviews. The new model significantly improves recommendation accuracy, especially with sparse customer ratings.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Science

Background:

  • Recommender systems are crucial for e-commerce growth, driving marketing success.
  • Collaborative filtering, using past customer ratings, is a common approach.
  • Sparse customer ratings (less than 4%) pose a significant challenge for traditional models.

Purpose of the Study:

  • To improve the accuracy of recommender systems, particularly in scenarios with sparse user data.
  • To address the limitations of traditional methods like TF-IDF and LDA in understanding product reviews.
  • To develop a novel approach combining matrix factorization with advanced natural language processing techniques.

Main Methods:

  • Integrated matrix factorization with Long Short-Term Memory (LSTM) networks and word embedding for product review interpretation.

Related Experiment Videos

Last Updated: Oct 9, 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

723
  • Utilized contextual understanding from product reviews to enhance rating predictions.
  • Compared the proposed model against traditional latent factor models.

    • Main Results:

    • The proposed model demonstrated a significant performance improvement of over 16% compared to traditional latent factor models.
    • Achieved an average RMSE (Root Mean Square Error) improvement of 1% over previous best performances.
    • Highlighted the importance of contextual insights from product reviews for sparse rating matrices.

    Conclusions:

    • The integration of matrix factorization with LSTM and word embedding offers a superior approach for e-commerce recommender systems.
    • Contextual understanding of product reviews is vital for overcoming data sparsity issues.
    • Future work should explore bidirectional word sequential models for further performance gains.