A Short Survey of Recommendation Technologies in Travel and Tourism

Recommendation has a long history as a successful application area of Artificial Intelligence. The demand of e-commerce platforms (e.g., amazon.com) to improve the accessibility of large productand service assortments contributed to an increased popularity of recommendation technologies. Three basic technologies supporting the personalized recommendation of products and services are presented in this paper. In order to take into account the focus of this special issue, we provide a discussion of the application of those technologies in the tourism domain (e.g., recommendation of travel destinations) with a special focus on mobile environments. Recommendation Technologies The increasing size and complexity of product assortments offered by e-commerce platforms requires appropriate technologies which alleviate the retrieval of products by online customers. Different recommendation technologies have been developed to help customers to easily find the best matching product. Those technologies have been successfully applied in different domains such as financial services, electronic goods, or movies. An overview of applications exploiting recommender technologies can be found in [16]. The most widespread technology is collaborative filtering (CF), which exploits user ratings of products in order to identify additional products that the active user may like as well [6]. User-based and item-based collaborative filtering are two basic variants of this technology. As shown in Figure 1, both variants are predicting to which extend the active user (in this case User3) would like currently unrated items. User-based approaches to collaborative filtering try to identify the k nearest neighbours of the active user (users having similar tastes), and calculate a prediction of the active user’s rating for a specific item. This rating can be defined, for example, as the weighted average of the k nearest neighbours’ ratings [6]. In the simplified example of Figure 1, User1 is found to be the nearest neighbour (k=1) of User3 (the active user) and his/her rating for the 4 product (‘Conspiracy Theory’) will be taken as prediction for the rating of User3 (rate=2). In contrast, item-based collaborative filtering is searching for items which received similar ratings from other users and were also (positively) rated by the active user. In the example