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Fig. 3. Karate club: participation of users in communities c 2 , c 4 .
Fig. 4. Karate club: participation of users in communities c 1 , c 3 .
4.2 Application of soft community detection for recommendation systems
In online social networks a recommendation of new social links may be seen as an attractive
service. Recently Facebook and LinkedIn introduced a service "People You May Know", which
recommends new connections using the friend-of-friend (FoF) approach. However, in large
networks the FoF approach may create a long and often not relevant list of recommendations,
which is difficult (and also computationally expensive, in particular in mobile solutions) to
navigate. Furthermore, in mobile social networks (e.g., Nokia portal Ovi Store) these kinds
of recommendations are even more complicated because users' affiliations to different groups
(and even its number) are not known. Hence, before making recommendations, communities
are to be detected first.
Recommendations as communities completion
Based on soft communities detection we suggest to make the FoF recommendations as follows:
(i) detect communities, e.g., by using one of the methods described above;
(ii) calculate membership g j
in all relevant communities for each node k ;
(iii) make new recommendations as communities completion following the rules below;
(
k
)
(iv) use multiple-membership to prioritize recommendations.
To make new link recommendations in (iii) we suggest the following rules:
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