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When E-Commerce Personalization Systems Show and Tell: Investigating the Relative Persuasive Appeal of Content-Based versus Collaborative Filtering
Mengqi Liao
,
S. Shyam Sundar
Film-Video and Media Studies
Research output
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Contribution to journal
›
Comment/debate
›
peer-review
78
Scopus citations
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Dive into the research topics of 'When E-Commerce Personalization Systems Show and Tell: Investigating the Relative Persuasive Appeal of Content-Based versus Collaborative Filtering'. Together they form a unique fingerprint.
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Keyphrases
Algorithm Accuracy
25%
Algorithm Design
25%
Artificial Intelligence
25%
Bandwagon Effect
25%
Between-subject
25%
Category Search
25%
Cognitive Heuristics
25%
Collaborative Filtering
100%
Content-based
100%
Content-based Filtering
50%
Electronic Commerce
100%
Experience Products
25%
Heuristic Model
25%
High Need
25%
Need for Cognition
25%
Personalization Systems
100%
Persuasive Appeals
100%
Positive Evaluation
25%
Product Category
50%
Product Recommendation
25%
Recommender Systems
25%
Search Products
25%
Show-and-Tell
100%
Social Psychology
25%
Strategic Communication
25%
Subject Experiment
25%
Systematic Processing
25%
User Preference
25%
Psychology
Artificial Intelligence
100%
Social Psychology
100%
Computer Science
Personalization System
100%
Product Experience
25%