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VAV6F
Youtube is the most video sharing and viewing platform in the world. As there are many people of different tastes, hundreds of categories of videos can be found on YouTube while thousands of videos of each. So, when the site recommends videos for a user it takes some issues which fill the needs of the user. Most of the time a user watches videos related to the previously watched video. But sometimes user’s mood changes with time or weather. A user may not hear a song in the whole year but can search the song on a rainy day. Another case a user may watch some types of videos at day but another type of videos at night or another at midnight. In this paper, we propose a recommendation system considering some attributes like weather, time, month to understand the dynamic mood of a user. Each attribute is assigned a weight calculated by performing a survey on some YouTube users.
Md. Shamim Reza Sajib. 2018. \u201cVideo Recommendation System for YouTube Considering Users Feedback\u201d. Global Journal of Computer Science and Technology - G: Interdisciplinary GJCST-G Volume 18 (GJCST Volume 18 Issue G1): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
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Total Score: 104
Country: Bangladesh
Subject: Global Journal of Computer Science and Technology - G: Interdisciplinary
Authors: Md. Shamim Reza Sajib, Md. Ariful Islam Malik, Md. Ashraful Islam, Sudip Kumar Halder (PhD/Dr. count: 0)
View Count (all-time): 265
Total Views (Real + Logic): 5990
Total Downloads (simulated): 1460
Publish Date: 2018 04, Wed
Monthly Totals (Real + Logic):
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Youtube is the most video sharing and viewing platform in the world. As there are many people of different tastes, hundreds of categories of videos can be found on YouTube while thousands of videos of each. So, when the site recommends videos for a user it takes some issues which fill the needs of the user. Most of the time a user watches videos related to the previously watched video. But sometimes user’s mood changes with time or weather. A user may not hear a song in the whole year but can search the song on a rainy day. Another case a user may watch some types of videos at day but another type of videos at night or another at midnight. In this paper, we propose a recommendation system considering some attributes like weather, time, month to understand the dynamic mood of a user. Each attribute is assigned a weight calculated by performing a survey on some YouTube users.
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