Recommendation Techniques for smart cities
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Journal Title
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Volume Title
Perustieteiden korkeakoulu |
Master's thesis
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Authors
Date
2017-08-28
Department
Major/Subject
Service Design and Engineering
Mcode
SCI3022
Degree programme
Master's Programme in ICT Innovation
Language
en
Pages
61
Series
Abstract
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 thesis, 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.Description
Supervisor
Smolander, KariThesis advisor
Horváth, TomásKeywords
event crawling, data extraction, source ranking, web extraction, web crawling