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Hybrid user perception model: Compar...

Wu, Mengqi.

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Hybrid user perception model: Comparing users' perceptions toward collaborative, content-based, and hybrid recommender systems.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hybrid user perception model: Comparing users' perceptions toward collaborative, content-based, and hybrid recommender systems./
Author:	Wu, Mengqi.
Description:	80 p.
Notes:	Source: Masters Abstracts International, Volume: 55-02.
Contained By:	Masters Abstracts International55-02(E).
Subject:	Communication. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1601780
ISBN:	9781339143224

Hybrid user perception model: Comparing users' perceptions toward collaborative, content-based, and hybrid recommender systems.
Wu, Mengqi.

Hybrid user perception model: Comparing users' perceptions toward collaborative, content-based, and hybrid recommender systems. - 80 p.

Source: Masters Abstracts International, Volume: 55-02.

Thesis (M.S.)--Iowa State University, 2015.

This study examines users' perceptions toward three types of recommender systems by employing a hybrid user perception model combining with Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM) in order to specifically explain a message-attitude-use process. Recommender systems, as an innovation applying big data ideas and algorithmic power, have been widely applied to multiple Internet industries. In order to further investigate how users perceived the use of recommender systems and the differences among users' perceptions toward the use of different recommender systems (collaborative filtering, content-based filtering, and hybrid filtering), three perception variables (perceived usefulness, perceived behavioral control, and perceived enjoyment) were specifically assessed by using an online survey of college students. Overall, the results indicated that there were some statistically significant differences among the user perceptions towards different types of recommender systems. In addition, users generally feel positive about the use of these recommender systems, and users' perceptions toward hybrid-filtering system were rated higher than perceptions toward collaborative filtering and content-based filtering.

ISBN: 9781339143224Subjects--Topical Terms:

524709
Communication.

Hybrid user perception model: Comparing users' perceptions toward collaborative, content-based, and hybrid recommender systems.
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245 1 0 $a Hybrid user perception model: Comparing users' perceptions toward collaborative, content-based, and hybrid recommender systems.
300 $a 80 p.
500 $a Source: Masters Abstracts International, Volume: 55-02.
500 $a Adviser: Jan Lauren Boyles.
502 $a Thesis (M.S.)--Iowa State University, 2015.
520 $a This study examines users' perceptions toward three types of recommender systems by employing a hybrid user perception model combining with Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM) in order to specifically explain a message-attitude-use process. Recommender systems, as an innovation applying big data ideas and algorithmic power, have been widely applied to multiple Internet industries. In order to further investigate how users perceived the use of recommender systems and the differences among users' perceptions toward the use of different recommender systems (collaborative filtering, content-based filtering, and hybrid filtering), three perception variables (perceived usefulness, perceived behavioral control, and perceived enjoyment) were specifically assessed by using an online survey of college students. Overall, the results indicated that there were some statistically significant differences among the user perceptions towards different types of recommender systems. In addition, users generally feel positive about the use of these recommender systems, and users' perceptions toward hybrid-filtering system were rated higher than perceptions toward collaborative filtering and content-based filtering.
590 $a School code: 0097.
650 4 $a Communication. $3 524709
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650 4 $a Technical communication. $3 3172863
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710 2 $a Iowa State University. $b Journalism and Mass Communication. $3 1030846
773 0 $t Masters Abstracts International $g 55-02(E).
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