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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230 Selected Studies on Software and Information Systems<br />
– Selection – web-based information systems often display a list of results, each with<br />
only a brief description. Selection of one particular result for further examination<br />
provides information about user’s interest.<br />
– Duration – as positive correlation was found between reading time and and explicit<br />
rating in USENET applications (as cited in [51]), we can consider duration as another<br />
aspect determining user’s interests.<br />
– Read wear – extends the duration aspect by including the idea of “computational<br />
wear”, which informs us about reading history of the document (e.g., which parts<br />
were read and which were just scrolled) [30].<br />
– Repetition – repetitive behavior in exploring information space.<br />
– Purchase – represents user’s decision to perform some additional action with the item<br />
such as purchase, print, add to bookmarks etc. It is a strong indication of a positive<br />
feedback.<br />
An example of work with implicit feedback is [17] in which authors were creating a model<br />
of a museum visitor to provide personalized reports about a particular museum visit. Each<br />
visitor possesses an electronic guide during the visit. Implicit feedback is represented by<br />
pressing keys “More” (positive feedback) or “Enough” (negative feedback) during the commentary.<br />
By pressing “More”, user gets further, more detailed information about particular<br />
exhibit. As a side effect, system is informed about user interests. By pressing “Enough”,<br />
user can interrupt the commentary on particular exhibit (which is interpreted as a negative<br />
feedback). Also the fact, that the user did not interrupt the commentary can be considered as<br />
rather positive feedback. The “More” button represents Selection from the above mentioned<br />
classification. Usage of “Enough” button is related to Duration.<br />
Implicit feedback was used also in project Casper [56] in the domain of job offers. Authors<br />
use three metrics to estimate implicit feedback: re-visiting an offer, time spent reading an<br />
offer and further activities related to an offer (applying for a job, sending an offer via e-mail).<br />
Focus is given on log preprocessing, to leave out unwanted events such as multiple clicks on<br />
one offer, which should not be considered as a re-visit if it was caused by user’s impatience<br />
or correction of an offer reading time according to mean reading time.<br />
The analysis of user behavior is discussed also in [38]. Authors examine time related<br />
information (implicit feedback Duration) in an e-Learning course, where students have possibility<br />
to listen to the lectures and watch handouts.<br />
Monitoring User Behavior This approach covers the implicit feedback acquisition approach,<br />
but is not restricted to feedback related behavior and tracks a complete behavior of<br />
the user within a system. The main difference is that the system records as many actions<br />
as possible such as the time the user spent viewing a particular page, the way the user<br />
navigates on a web site, clickstreams, list – detail transactions, page reading (i.e., scrolling<br />
down the page), eventually usage of active page element (e.g., hover) or additional system’s<br />
functionality (e.g., adding a publication to Favorites).