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218455

An Efficient Content-Based Video Recommendation

Article

Last updated: 24 Dec 2024

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Abstract

In a world full of online videos, it is really hard to find relevant content as the data is simply too much. A recommendation system was created to refine this experience, to match relevant content to an interested user. Most recommending systems use algorithms, calculations, and implicit feedback. These methods are effective unless the video does not have implicit feedback in which the algorithms will mostly fail to get relevant content. This is known as cold-start that affects newly uploaded videos, since they start without any data or user comments. Another problem facing users every day is finding the content they want, because it is dependent on videos having labels or having many user views. Since the search engine's mechanism uses the tags and keywords inserted for the video rather than the actual content in it. In this paper, a recommendation system by content is proposed, the system detects the objects and sounds inside the video, and also adds the feature to search using uploaded scenes or filter scenes based on keyword inputted.  More experimental results have been done with various scenarios to demonstrate the effectiveness of the proposed system in terms of video recommendation by content

DOI

10.21608/jocc.2022.218455

Keywords

Feature extraction Video recommendation Video streaming Cold, Start Sound detection Dynamic time warping algorithm

Authors

First Name

walaa

Last Name

hassan

MiddleName

-

Affiliation

Department of Computer Science, Faculty of Computer Science & Informatics, Suez Canal University, Cairo, Egypt

Email

walaa.hassan@miuegypt.edu.eg

City

-

Orcid

0000-0003-0142-0632

First Name

Youssef

Last Name

Roshdy

MiddleName

-

Affiliation

Computer Science Department, Misr international university

Email

youssefroshdy2@gmail.com

City

-

Orcid

-

First Name

Mennat Allah

Last Name

Hassan

MiddleName

-

Affiliation

Faculty of Computer Science, Misr International University.

Email

mennatallah.sayed@miuegypt.edu.eg

City

-

Orcid

-

First Name

foad

Last Name

osama

MiddleName

-

Affiliation

Computer science department , misr international university

Email

foad1611019@miuegypt.edu.eg

City

-

Orcid

-

Volume

1

Article Issue

1

Related Issue

31132

Issue Date

2022-02-01

Receive Date

2022-01-20

Publish Date

2022-02-01

Page Start

48

Page End

64

Online ISSN

2636-3577

Article File

JOCC_Volume 1_Issue 1_Pages 48-64.pdf

PDF . 1.5MB

Link

https://jocc.journals.ekb.eg/article_218455.html

Detail API

https://jocc.journals.ekb.eg/service?article_code=218455

Order

5

Type

Original Article

Type Code

731

Publication Type

Journal

Publication Title

Journal of Computing and Communication

Publication Link

https://jocc.journals.ekb.eg/

MainTitle

An Efficient Content-Based Video Recommendation

Details

Type

Article

Created At

22 Jan 2023