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User Modeling for Personalized Web-Based Systems 243
Web
Crawler
Classifier
Paper
extractor
Author
extractor
Conference
extractor
Location
extractor
Paper
aggregator
Author
aggregator
Conference
aggregator
Location
aggregator
Scientific
Object
warehous
Figure 8-9. Architecture of Scientific Papers Extraction System (according to [50]).
good results in scientific papers domain. If applied to multiple domain, the system could
possibly provide enough information about user to overcome the cold-start problem.
Social Networks Discovering The paper [55] proposes the idea, that each recommender
system naturally fosters communities of users. It is exactly the community which drives the
recommendation once enough users are engaged and modeled (see Figure 8-10).
However, as an implicit user modeling does not make the social network identification
salient, authors propose to employ other techniques to discover social networks:
– Link Analysis and Cyber-Communities – evidence of community existence is often implicit
in data, such as communications logs and webpages. These are fertile reflections
of natural connectivity among people. Some recommender systems require users to
create and maintain profiles. On the other hand, approaches which model people connections
or social organization result in representations which are likely to be more
accurate reflections than a user’s perception of his own connections [55].
– Mining and Exploiting Structure – social networks also can be formed by applying transformations
on other, typically bipartite, graph representations identified in datasets.