Many links between Web pages can be viewed as indicative of the quality and importance of the pages they pointed to. Accordingly, several studies have proposed metrics based on links to infer web page content quality. However, as far as we know, the only work that has examined the correlation between such metrics and content quality consisted of a limited study that left many open questions. Despite the fact that these metrics showed to be successful in the task of ranking pages provided as answers to queries submitted to search engines, it is not possible to determine the specific contribution that factors such as quality, popularity, and importance have on the results. This difficulty is partially due to the fact that such information about Web pages in general is hard to obtain. Unlike ordinary Web pages, the quality, importance and popularity of Wikipedia articles are evaluated by human experts or might be easily estimated. Thus, it is feasible to verify the relationship between link analysis metrics and the afore mentioned factors in Wikipedia articles. This is our goal in this work. In order to accomplish that, we implemented several link analysis algorithms and compared their resulting rankings with the ones created by human evaluators regarding factors such as quality, popularity and importance. We found that the metrics are more correlated to quality and popularity than to importance, and that the correlations are moderate.

No abstract

The tourism sector in Brazil has grown considerably in recent years. Despite this growth, the sector still presents several problems such as the lack of information in Portuguese and in other languages for Brazilian and foreign tourists. The absence of information about tourist sites and ordinary services also affects individuals when settling in a new city, as it is the case when freshman students move to a new city to start their studies in a college or university. In this work, we propose an innovative vision of a context-aware platform for recommending points of interest in Brazilian cities, designed with mechanisms for collecting data from the web, for extracting points of interest and background information, and for learning context-aware recommendation models. The platform is accessed by a mobile application. To validate our proposal, we ran a case study where freshman students used the platform during their first months in a new city.