Destination similarity based on implicit user interest
This work addresses the problem of destination recommendation for users in the travel industry, but it appears incremental as it builds on existing similarity measures without introducing a major breakthrough.
The authors tackled the challenge of analyzing sparse and dynamic online travel data by proposing a new similarity method based on implicit user interest, achieving significant improvement over existing similarity measures in recommender systems.
With the digitization of travel industry, it is more and more important to understand users from their online behaviors. However, online travel industry data are more challenging to analyze due to extra sparseness, dispersed user history actions, fast change of user interest and lack of direct or indirect feedbacks. In this work, a new similarity method is proposed to measure the destination similarity in terms of implicit user interest. By comparing the proposed method to several other widely used similarity measures in recommender systems, the proposed method achieves a significant improvement on travel data. Key words: Destination similarity, Travel industry, Recommender System, Implicit user interest