Using collective intelligence to generate trend-based travel recommendations

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 recommendations are rated better than user-generated recommendations.