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Talks on Recommender Systems and Data Extraction
Thursday, July 1st, Seminarroom 184/2
14:00 (s.t.) Cai Ziegler, Albert-Ludwigs University Freiburg:
"Crafting Decentralized Recommender Systems"
Endeavors investigating recommender systems have primarily addressed
centralized scenarios and largely ignored open, decentralized systems,
where remote information distribution and sparseness prevails. The absence
of superordinate authorities having full access and control introduces
serious issues necessitating novel approaches. Hence, our primary objective
targets the design and conception of suchlike paradigms, relying upon two
pivotal components, namely social network analysis and taxonomy-driven
information filtering. Likewise applicable to other domains and scenarios,
those two complementary pillars are seamlessly integrated into one
coherent framework, eventually paving the way for decentralized recommenders.
15:00 Kai Simon, Albert-Ludwigs University Freiburg:
"Automatic Data Extraction (Crawling the hidden web)"
To address the problem of web data extraction, several techniques have been
proposed in the literature. Using the taxonomy for characterizing web data
extraction tools, the technique described in this talk falls into the
"HTML-aware" category. Hereby the extraction rules are generated
automatically without any learning or human intervention according to the
DOM tree, and the tag token sequence, respectively. The main assumptions
for this strategy follows the idea that the target pages were dynamically
generated from data sources by one common schema and they contain data
objects with a common HTML tag sequence. By analyzing several pages
belonging to the same schema one tries to infer a regular expression which
is able to extract the data of interest. In this talk we try to solve this
problem with methods known from bioinformatics tailored for our purposes.