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User Modeling for Personalized Web-Based Systems 233
An example of a user model bootstrap based on social relationships can be found in [45].
Authors consider trust relationships between users and its propagation. A trust statement
made by user A towards user B means that user A consistently finds the reviews and ratings
of user B valuable. Trust statements from all users form a web of trust, or trust (social)
network (this trust network is always personalized, from the point of view of an individual
node (subject)).
The whole idea is to replace similarity between users (computed according to their
ratings of the same item) in the recommendation algorithm by the trust between users
(which is propagated as soon as new user defines her trust to one already existing user and
is further refined as the user trust or distrust others).
We believe that the approach could benefit from the exploit of classical studies of human
social interactions – social network analysis. Its goal is determining the functional roles of
individuals in a social network and diagnosing network-wide conditions or states. Different
individuals in a social network often fulfill different roles. Examples of intuitive roles
include leaders, followers, regulators, “popular people” and early adopters [44]. These
roles can influence the computation of the trust in the community and its spreading (e.g.,
trust of a leader towards a particular user is of greater value than a trust between ordinary
users).
As we mentioned, models of social networks could overtake the role of the user model,
which shrink to one information expressing membership in a network. We can find similarity
of an approach built upon social networks with stereotype-based approach. However, where
stereotypes are usually statically defined and users are assigned a stereotype only once, at
the very beginning of their work with the system, the communities are usually discovered
“on-the-fly”, have their proper characteristics and memberships of the user to the community
is decided automatically.
An example of a system which employes community-based personalization is Community-based
Conference Navigator [27]. It uses social navigation support to help conference attendees
to plan their attendance at the most appropriate sessions and make sure that the most
important papers are not overlooked.
The system provides additional service as an adaptive layer over AACEE conference
planning system, which allows the conference attendees to browse the conference program
and plan their schedule. Community-based conference navigator tracks different activities of
the community (such as paper scheduling) and allows users to add comments to papers.
All activities result in updates of the community profile, which accumulates over time the
“wisdom” of the community. The community profile and “wisdom” is used in the adaptation
of the original system by adding adaptive icon annotations.
The selection of the community is done manually by each user. If the user does not find the
suitable community, she is allowed to create a new one. Moreover, user can switch between
communities anytime during the usage of the system, which gives instantly the annotations
for a different community. However, it seems that user can act only as a member of one
community at a time, so all actions contribute only to one community profile. However, many
people belong to several communities and act as “bridges” between different communities,
so it would not be easy for them to choose strictly one. It would be interesting to combine
the community-based adaptation with the traditional personalization based on user model
(which can nevertheless provide detailed characteristics).