In this paper, propose a cocktail approach to generate the lists for personalized travel package recommendation. Furthermore, we extend the TAST model to the tourist-relation-area-season topic (TRAST) model for capturing the latent relationships among the tourists in each travel group. Finally, we evaluate the TAST model, the TRAST model, and the cocktail recommendation approach on the real-world travel package data.
TRAST model can be easily extended for computing relationships among many more tourists. However, the computation cost will also go up. To simplify the problem, in this paper, each time we only consider two tourists in a travel group as a tourist pair for mining their relationships. By this TRAST model, all the tourists’ travel preferences are represented by relationship distributions.
We can use their relationship distributions as features to cluster them, so as to put them into different travel groups. Thus, in this scenario, many clustering methods can be adopted. Since choosing clustering algorithm is beyond the scope of this paper, in the experiments, we refer to K-means one of the most popular clustering algorithms. A Cocktail Approach for Travel Package Recommendation
We first analyze the characteristics of the existing travel packages and develop a tourist-area-season topic (TAST) model. This TAST model can represent travel packages and tourists by different topic distributions, where the topic extraction is conditioned on both the tourists and the intrinsic features (i.e., locations, travel seasons) of the landscapes a tourist-area-season topic (TAST) model which can represent travel packages and tourists by different topic distributions. In the TAST model, the extraction of topics is conditioned on both the tourists and the intrinsic features (i.e., locations, travel seasons) of the landscapes.
As a result, the TAST model can well represent the content of the travel packages and the interests of the tourists. Based on this TAST model, a cocktail approach is developed for personalized travel package recommendation by considering some additional factors including the seasonal behaviors of tourists, the prices of travel packages, and the cold start problem of new packages. Finally, the experimental results on real-world travel data show that the TAST model can effectively capture the unique characteristics of travel data and the cocktail recommendation approach performs much better than traditional techniques.
We propose the tourist-relation-area-season topic (TRAST) model, which helps understand the reasons why tourists form a travel group. This goes beyond personalized package recommendations and is helpful for capturing the latent relationships among the tourists in each travel group. In addition, we conduct systematic experiments on the real world data. These experiments demonstrate that the TRAST model can be used as an assessment for travel group automatic formation but also provide more insights into the TAST model and the cocktail recommendation approach.
Our contributions of the cocktail approaches, and the TRAST model for travel package recommendations are each dashed rectangular box in the dashed circle identifies a travel group and the tourists in the same travel group are represented in this TRAST model, all the tourists’ travel preferences are represented by relationship distributions. For a set of tourists, who want to travel the same package, we can use their relationship distributions as features to cluster them, so as to put them into different travel groups. Thus, in this scenario, many clustering methods can be adopted. Since choosing clustering algorithm is beyond the scope of this paper, in the experiments, we refer to K-means one of the most popular clustering algorithms.