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iTEC Project
Title: ITEC-D10_2_V1-1 041102012.Docx
∑
∑
(7)
where:
0 if data are registered as a consequence of an enrichment process. 1
otherwise.
Number of relevant values for a given property, as specified by the requirement
Resource level for value
Number of relevant values semi-automatically included by the system as a
consequence of an enrichment process.
Level inferred from source k for value j
Confidence level of source
o Based on user ratings (collaborative properties): given a collaborative property 15 on
which we establish a criterion, we compute the arithmetical average of ratings ( )
provided by users ().
(8)
A “neutral” value of will be assigned to those resources without any user
rating. Thus, those resources that have not yet been rated would not be penalized
against those with a negative rating ( ).
This strategy is applied to criteria related to resources’ rating factors (tool,
person, event, ). As an example, a tool rated by three different users
with scores 3, 7, 8, will get value
.
For this type of criterion, we consider the possibility of including a confidence factor
( ) to weight users’ ratings. In this case, we apply the formula below:
∑
∑
According to the SDE data model for Year One, which considers three types of resources
(tool, person, event), we define three different families of criteria (cf. 1.2.2.3), one for each
type of resource. Taking into account the impact that criteria have on the implementation of
the recommender, we devote the next section to discussing this matter.
(9)
15 A collaborative property is a property whose values are assigned by users. It supports relations such as
resource X has been rated by user U with score V.
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