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Identifying Online Streaming User Va...
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Yan, Jingyu.
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Identifying Online Streaming User Value in the Netflix Recommendation System.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Identifying Online Streaming User Value in the Netflix Recommendation System./
作者:
Yan, Jingyu.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:
67 p.
附註:
Source: Masters Abstracts International, Volume: 57-01.
Contained By:
Masters Abstracts International57-01(E).
標題:
Multimedia communications. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10634108
ISBN:
9780355288001
Identifying Online Streaming User Value in the Netflix Recommendation System.
Yan, Jingyu.
Identifying Online Streaming User Value in the Netflix Recommendation System.
- Ann Arbor : ProQuest Dissertations & Theses, 2017 - 67 p.
Source: Masters Abstracts International, Volume: 57-01.
Thesis (M.S.)--Drexel University, 2017.
Netflix is one of the most successful providers of Over-The-Top content, delivered via the internet. By using a massive amount of data generated by its streaming users, a personalized recommendation system is one of Netflix's value propositions. However, due to a lack of publically available data and studies, it is difficult to determine whether its recommendation system has brought true value to streaming users, and how important this feature is to its users.
ISBN: 9780355288001Subjects--Topical Terms:
590562
Multimedia communications.
Identifying Online Streaming User Value in the Netflix Recommendation System.
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Netflix is one of the most successful providers of Over-The-Top content, delivered via the internet. By using a massive amount of data generated by its streaming users, a personalized recommendation system is one of Netflix's value propositions. However, due to a lack of publically available data and studies, it is difficult to determine whether its recommendation system has brought true value to streaming users, and how important this feature is to its users.
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The purpose of this study is to evaluate the recommendation system from the streaming users' point of view. By collecting survey results from 119 participants, this study will attempt to reveal the relationship between streaming users and the personalized recommendation system, and to show whether or not streaming users are satisfied with this feature.
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