Authors:
Sabine Schlick
;
Isabella Eigner
and
Alex Fechner
Affiliation:
Friedrich-Alexander University, Germany
Keyword(s):
Trend-based Recommender System, Spatio-Temporal Travel Trends, Individualized Travel Recommendations, Collective Intelligence.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Data Analytics
;
Data Engineering
;
Data Mining in Electronic Commerce
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
;
User Profiling and Recommender Systems
Abstract:
Trips are multifaceted, complex products which cannot be tested in advance due to their geographical distance.
Hence, making a travel decision people often ask others for advice. This leads to an increasing importance of
communities. Within communities people share their experiences, which results in new, more extensive
knowledge beyond the individual knowledge of each member. The objective of this paper is to use this
knowledge by developing an algorithm that automatically generates trend-based travel recommendations.
Based on the travel experiences of the community members, interesting travel areas are identified. Five key
figures to evaluate these areas according to general criteria and the users’ individual preferences are
developed. The algorithm allows to generate recommendations for the whole community and not only for
highly active members, resulting in a high coverage. A study conducted within an online travel community
shows that automatically generated, trend-based trip re
commendations are rated better than user-generated
recommendations.
(More)