Movie Recommender Systems: Concepts, Methods, Challenges, and Future Directions

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F22%3A10250026" target="_blank" >RIV/61989100:27230/22:10250026 - isvavai.cz</a>

Result on the web

<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:000823874200001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:000823874200001</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s22134904" target="_blank" >10.3390/s22134904</a>

Result language

angličtina

Original language name

Movie Recommender Systems: Concepts, Methods, Challenges, and Future Directions

Original language description

Movie recommender systems are meant to give suggestions to the users based on the features they love the most. A highly performing movie recommendation will suggest movies that match the similarities with the highest degree of performance. This study conducts a systematic literature review on movie recommender systems. It highlights the filtering criteria in the recommender systems, algorithms implemented in movie recommender systems, the performance measurement criteria, the challenges in implementation, and recommendations for future research. Some of the most popular machine learning algorithms used in movie recommender systems such as K-means clustering, principal component analysis, and self-organizing maps with principal component analysis are discussed in detail. Special emphasis is given to research works performed using metaheuristic-based recommendation systems. The research aims to bring to light the advances made in developing the movie recommender systems, and what needs to be performed to reduce the current challenges in implementing the feasible solutions. The article will be helpful to researchers in the broad area of recommender systems as well as practicing data scientists involved in the implementation of such systems. (C) 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20300 - Mechanical engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Sensors

ISSN

1424-3210

e-ISSN

1424-8220

Volume of the periodical

22

Issue of the periodical within the volume

13

Country of publishing house

CH - SWITZERLAND

Number of pages

22

Pages from-to

nestrankovano

UT code for WoS article

000823874200001

EID of the result in the Scopus database

2-s2.0-85133024440