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

Factorial Design02:01

Factorial Design

13.3K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
13.3K
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

76
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
76
Cluster Sampling Method01:20

Cluster Sampling Method

12.9K
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...
12.9K
Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

181
Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
181
Manipulation and Analysis01:21

Manipulation and Analysis

72
GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
72
Passive Filters01:27

Passive Filters

619
Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
619

You might also read

Related Articles

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

Sort by
Same author

RBMP2 shapes specialized membranes for CO₂ delivery in the pyrenoid condensate.

bioRxiv : the preprint server for biology·2026
Same author

Electrochemical α-Arylation of Quinolinones with Site-Selective Dehydrogenative Coupling.

Organic letters·2026
Same author

Disulfide modification and thiol protection via tris(trimethylsilyl)silane-mediated hydrosilylation of disulfides.

Nature communications·2026
Same author

Carbene-Catalyzed C-H Arylation of Aldehyde-Hydrazone via Radical Cross-Coupling with Iodobenzene.

Organic letters·2026
Same author

Age-related aberrant alternative splicing as a prognostic tool in older breast cancer patients.

Communications biology·2025
Same author

<i>N</i>-Heterocyclic Selone-Catalyzed [2σ + 2π] Cycloaddition of Bicyclo[1.1.0]butanes via Photoredox Radical Buffer.

Journal of the American Chemical Society·2025

Related Experiment Video

Updated: Sep 24, 2025

Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model
06:30

Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model

Published on: May 24, 2019

5.4K

Collaborative Filtering Algorithm-Based Destination Recommendation and Marketing Model for Tourism Scenic Spots.

Kejun Lin1, Shixin Yang1, Sang-Gyun Na1

  • 1College of Business Administration, Wonkwang University, 460 Iksandae-ro, Iksan, Jeonbuk, Republic of Korea.

Computational Intelligence and Neuroscience
|May 9, 2022
PubMed
Summary

This study enhances travel recommendations by optimizing collaborative filtering algorithms (CFA) to overcome information overload and data sparsity, improving user satisfaction and travel planning.

More Related Videos

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.5K

Related Experiment Videos

Last Updated: Sep 24, 2025

Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model
06:30

Spotlighting Customers' Visual Attention at the Stock, Shelf and Store Levels with the 3S Model

Published on: May 24, 2019

5.4K
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.5K

Area of Science:

  • Tourism and Hospitality Management
  • Information Science
  • Computer Science

Background:

  • The digital age presents abundant travel information, leading to information overload and unmet personalized user needs.
  • Traditional collaborative filtering algorithms (CFA) face data sparsity issues with increasing user bases.
  • Existing recommendation systems struggle to balance user satisfaction and diverse travel preferences.

Purpose of the Study:

  • To optimize the collaborative filtering algorithm (CFA) for personalized travel recommendations.
  • To address information overload and data sparsity in the tourism industry.
  • To enhance the travel experience through a satisfaction balance strategy.

Main Methods:

  • Optimized the collaborative filtering algorithm (CFA) by incorporating similarity and correlation factors.
  • Implemented a satisfaction balance strategy to improve recommendation relevance.
  • Conducted experiments to evaluate the performance of the improved CFA method.

Main Results:

  • The improved CFA method demonstrated the highest average accuracy across the dataset.
  • The satisfaction balance strategy significantly enhanced recommendation performance.
  • The optimized model effectively addresses information overload and data sparsity.

Conclusions:

  • The enhanced CFA model provides superior travel recommendations for users.
  • The satisfaction balance strategy improves the overall travel experience.
  • This recommendation model aids user attraction selection and travel company marketing optimization.