Abstract
Online “best places to visit” recommendations in an independent travelers' community are gaining more and more popularity due to the various and differentiated individual needs. Therefore, this research aims to identify heuristic factors affecting the popularity of “best places to visit” recommendations. The heuristic factors are derived from both recommender-related (recommender identity disclosure, recommender reputation, recommender experience, recommender location) and recommendation-related (recommendation helpfulness, recommendation comment, recommendation length, recommendation place) heuristic perspectives based on the heuristic-systematic model. The implications of this research are expected to contribute to build one more layer to the literature on both the destination recommendation and recommendation popularity, and practically, to make more favorable “best places to visit” recommendations in the context of Chinese outbound independent traveler communities.
Original language | English |
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Title of host publication | Proceedings of the International Conference on Electronic Commerce, ICEC 2017 |
Publisher | Association for Computing Machinery |
ISBN (Electronic) | 9781450353120 |
DOIs | |
State | Published - 17 Aug 2017 |
Externally published | Yes |
Publication series
Name | ACM International Conference Proceeding Series |
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Bibliographical note
Publisher Copyright:© Copyright 2017 ACM
Keywords
- Best places to visit
- Destination recommendation
- Heuristic-systematic model
- Recommendation popularity
- Recommendation-related heuristic factors
- Recommender-related heuristic factors
ASJC Scopus subject areas
- Software
- Human-Computer Interaction
- Computer Vision and Pattern Recognition
- Computer Networks and Communications