On Using Social Context to Model Information Retrieval and Collaboration in Scientific Research Community - Community-Built Databases: Research and Development - page 151

Fig. 6.5 Tuning the
a

0,1

parameter for PR-Docs model

0,09

0,08

0,07

0,06

0,05

p@0.1
p@0.2

0,04

0,03

0,02

0,01

0

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1

α

Fig. 6.6 Evaluation of the
retrieval effectiveness

0,14

0,12

0,10

TF*IDF
PR- Do c s
Kirsch
SM0,5
SM0,6

0,08

0,06

0,04

0,02

0

p@0,1 p@0,2

We note that the best values of the parameter can lead to an improvement in favor

of our model between 15 and 55% compared with the baseline TF

a

IDF. There-
fore, we confirm that integrating the social relevance of a document can signifi-
cantly improve the retrieval effectiveness.
Comparing our model with best obtained values of the PR-Docs model, we note
an improvement of 45% for p @0.1. Therefore, we conclude that the W-Hub measure
computed on the social network of authors better expresses the social importance of
scientific papers than prior measures based on the citation graph.
Comparing our model with the Kirsch's model, we note an improvement of 14%
that confirms the impact on the retrieval performances of including the citation
links and weighting the social network edges.
In summary, results show a low performance for the retrieval systems used for
comparison. In fact, we used tags for experimental evaluation which are user-
generated terms and may not be present in document content. Therefore, only a
few relevant documents can be retrieved which explains the low precisions of the
proposed model. Furthermore, results are proportional to the content-based retrieval
model used to compute the topical relevance of documents and its performance
directly affects the extended models. The main objective of previous experiments is
to improve content-based raking by including the social importance of document
and this is achieved with a significant improvement of 55% compared with the
TF

ID baseline.