elektronická verzia publikácie - FIIT STU - Slovenská technická ...
elektronická verzia publikácie - FIIT STU - Slovenská technická ...
elektronická verzia publikácie - FIIT STU - Slovenská technická ...
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236 Selected Studies on Software and Information Systems<br />
and provides a ranked list of similar users from the very beginning of user’s work with the<br />
system (so the system is able to provide also collaborative filtering based on users similarity).<br />
During the usage of the system, user browsing behavior is monitored via a proxy server,<br />
where each URL browsed during normal work activity is logged. A classifying algorithm is<br />
used to classify browsed URL’s based on a training set of labeled example papers, storing<br />
each new paper in a central database. The profiling algorithm performs correlation between<br />
paper topic classifications and user browsing logs. Whenever a research paper is browsed<br />
that has been classified as belonging to a topic or an explicit feedback on recommendation is<br />
provided, it accumulates an interest score for that topic. This is the way Quickstep contributes<br />
back to the bootstrap ontology.<br />
The weak point of the approach is the assumption that ontology used for bootstrap contains<br />
knowledge about the new user and that this user has already written some publications<br />
which could be compared with publications in the information space.<br />
User Model Mediation<br />
The concept of mediation of partial user models between several adaptive systems was proposed<br />
in [8]. The basic idea is that different applications would benefit from enriching their<br />
UMs through importing and aggregating user models which were built by other, possibly<br />
related applications. It is different from the centralized user model approach using user<br />
modeling server, where each application extracts the required data from the central UM<br />
and updates it later. The described approach is designed to be decentralized and providing<br />
ad-hoc (for a specific purpose) generation of user model for the target application through<br />
translation and aggregation of partial UMs built by other applications. It is therefore useful<br />
for solving cold-start problem.<br />
The mediation to be successful needs to solve several issues among which is user’s<br />
privacy, the structural heterogeneity and incompleteness of the user models content, since<br />
every application refers to a specific application domain only.<br />
The mediation process is partitioned to the following stages (see Figure 8-8):<br />
1. A target application, required to provide personalization to a user, queries the mediator<br />
for the user model related to the application domain.<br />
2. The mediator identifies the required personalization domain and the user model<br />
representation in the target application.<br />
3. The mediator determines a set of other applications that can potentially provide partial<br />
domain-related user models of the given user and queries them.<br />
4. Applications, actually storing the needed data, answer the query, and send to the<br />
mediator their partial user models of the given user.<br />
5. The mediator translates and aggregates the acquired partial user models (using the<br />
knowledge base (KB)) into a single domain-related user model, represented according<br />
to the target application.<br />
6. The generated domain-related user model is sent to the target application, which is<br />
capable of providing more accurate personalized service.