Evaluating Data Sources for Crawling Events from the Web

The bottleneck of event recommender systems is the availability of actual, up-to-date information on events. Usually, there is no single data feed, thus information on events must be crawled from numerous sources. Ranking these sources helps the system to decide which sources to crawl and how often. In this paper, a model for event source evaluation and ranking is proposed based on well-known centrality measures from social network analysis. Experiments made on real data, crawled from Budapest event sources, shows interesting results for further research.