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

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

4.3K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
4.3K
Cluster Sampling Method01:20

Cluster Sampling Method

15.3K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
15.3K
Stratified Sampling Method01:16

Stratified Sampling Method

15.9K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
15.9K
Distance Problem01:29

Distance Problem

112
When an object's velocity changes over time, the total distance traveled can be determined by summing small displacement intervals over short increments. This approach approximates the true distance through numerical summation and the use of integral calculus. An estimate of the total displacement can be obtained by measuring velocity at regular intervals and multiplying each value by the corresponding time step.If a runner accelerates over the first three seconds of a race, speed measurements...
112
Sieve Analysis and Grading Curves01:19

Sieve Analysis and Grading Curves

1.1K
Sieve analysis is a method used to determine the particle size distribution of aggregate materials. This process involves the following steps:
1.1K
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

554
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
554

You might also read

Related Articles

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

Sort by
Same journal

RETRACTION: Real-Time Modulation of Physical Training Intensity Based on Wavelet Recursive Fuzzy Neural Networks.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026
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
See all related articles

Related Experiment Video

Updated: Mar 9, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

Preference Mining Using Neighborhood Rough Set Model on Two Universes.

Kai Zeng1

  • 1Faculty of Information Engineering, Guizhou Institute of Technology, No. 1 Caiguan Road, Guiyang 550003, China.

Computational Intelligence and Neuroscience
|January 4, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new model, the parametric neighborhood rough set on two universes (NRSTU), to address the cold-start problem in preference mining for e-commerce and video platforms. NRSTU significantly enhances recommendation accuracy for new users and items.

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.6K

Related Experiment Videos

Last Updated: Mar 9, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.6K

Area of Science:

  • Artificial Intelligence
  • Data Mining
  • Recommender Systems

Background:

  • Preference mining is crucial for user satisfaction and loyalty in e-commerce and video platforms.
  • Classical methods struggle with the cold-start problem for new users or items.
  • Effective preference mining requires robust user and item data modeling.

Purpose of the Study:

  • To propose a novel model for preference mining that effectively handles the cold-start problem.
  • To enhance recommendation accuracy and user experience in e-commerce and video platforms.
  • To provide a framework for describing user and item data structures and defining preference rules.

Main Methods:

  • Developed a parametric neighborhood rough set on two universes (NRSTU) model.
  • Utilized the neighborhood lower approximation operator to define preference rules.
  • Implemented NRSTU for item recommendation based on derived preference rules.

Main Results:

  • The NRSTU model provides an effective solution for preference mining, particularly in cold-start scenarios.
  • Experimental results demonstrate a significant improvement in recommendation accuracy.
  • NRSTU achieved approximately 19% higher recommendation accuracy compared to traditional methods.

Conclusions:

  • The proposed NRSTU model offers a viable and effective approach to preference mining.
  • NRSTU successfully addresses the limitations of traditional methods in cold-start situations.
  • This research contributes to improved personalization and user engagement in online platforms.