Degree Type

Thesis

Date of Award

2015

Degree Name

Master of Science

Department

Greenlee School of Journalism and Communication

Major

Human Computer Interaction

First Advisor

Jan Lauren Boyles

Abstract

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.

DOI

https://doi.org/10.31274/etd-180810-4288

Copyright Owner

Mengqi Wu

Language

en

File Format

application/pdf

File Size

80 pages

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