Recommender systems play a key role in the decision making process of users in Web systems. In tourism, it is widely used to recommend hotels, tourist attractions, accommodations, etc. In this paper, we present a personalized neighborhood-based method to recommend cities. This is a fundamental problem whose solution support other tourism recommendations. Our recommendation approach takes into account information of two different layers, namely, an upper layer composed by cities and a lower layer composed by attractions of each city. It consists of first building a social network among users, where the edges are weighted by the similarity of interests between pairs of users, and then using this network as a component of a collaborative filtering strategy to recommend cities. We evaluate our method using a large dataset collected from TripAdvisor. Our experimental results show that our approach, despite being simple, outperforms the precision achieved by a state-of-the-art baseline approach for implicit feedback (WRMF), which exploits only the overall popularity of cities. We also show that the use of a secondary layer (attraction) contributes to improve the effectiveness of our approach.
Recently it has been observed a world wide increase of online sales, mainly due to agility to buy and attractive prices that are offered on the Web. However, fraud has also been increased on the same rate or more. In order to address this problem it is very important to understand the characteristics of fraudsters and their typical behavior. On the tourism e-market it is not different, thus millions of frauds occur each year. In this work we analyze a representative amount (thousands) of online transactions of a tourism Web system. We try to understand the characteristics of fraudsters with the main goal to support decision of e-payment evaluation of transactions. Our results are promising, achieving up to 64% of increase in accuracy in comparison to the baseline.
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